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Published Project Report PPR473

A city-wide road traffic emission model for Oxford – scoping report

P G Boulter, I S McCrae, J Price and P Emmerson

Transport Research Laboratory

PUBLISHED PROJECT REPORT PPR473

A city-wide road traffic emission model for Oxford - scoping report

by P G Boulter, I S McCrae, J Price and P Emmerson

Prepared for: Project Record: Definition of a city-wide road traffic emission model

Client: Oxford City Council

Roger Pitman

Copyright Transport Research Laboratory January 2012

This Published Report has been prepared for Oxford City Council. Published Project Reports are written primarily for the Client rather than for a general audience and are published with the Client’s approval.

The views expressed are those of the authors and not necessarily those of Oxford City Council.

Name Date

Approved

Project Manager

Karen Winkle 19/4/2010

Technical Referee

Melanie Hobson 19/4/2010

Published Project Report

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When purchased in hard copy, this publication is printed on paper that is FSC (Forest Stewardship Council) and TCF (Totally Chlorine Free) registered.

Contents Amendment Record This report has been issued and amended as follows

Version Date Description Editor Technical Referee

1 19/04/10 First draft Paul Boulter Melanie Hobson

2 22/03/11 Client report Paul Boulter Melanie Hobson

3 13/01/12 Published report (content as version 2)

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Contents

Executive summary

1 Introduction 1

1.1� Background and objectives 1�

1.2� Approach and report structure 2�

2 Monitoring and reporting obligations relating to road traffic and/or air pollution 4

2.1� Overview 4

2.2� Local Air Quality Management (LAQM) 4�

2.3� National indicator for air quality 5�

2.4� National indicators for climate change 5�

2.5� Local Transport Plans (LTPs) 6�2.5.1� Background 6�2.5.2� The Oxfordshire County Council LTPs 7�2.5.3� Monitoring of LTPs 7�

2.6� Assessing sustainable development 8�

2.7� Implications for the Oxford city-wide model 9�

3 General modelling considerations 10�

3.1� Transport modes 10�

3.2� Potential applications 10�

3.3� Geographical extent 11�

3.4� Road network and spatial resolution 11�

3.5� Modelled years 12�

3.6� Temporal resolution 12�

3.7� Software and data manipulation 13�

3.8� Quality assurance 13�

4 Traffic modelling considerations 14�

4.1� Overview 14�

4.2� Modelling approaches 14�4.2.1� Junction-based traffic models 14�4.2.2� Traffic assignment models 15�4.2.3� Micro-simulation traffic models 15�

4.3� Traffic models currently used in Oxford 16�4.3.1� Central Oxfordshire Traffic Model 16�4.3.2� City-centre VISSIM model 19�4.3.3� Central Oxfordshire AIMSUN Model 19�4.3.4� INTRA-SIM 21�4.3.5� VIBAT 21�

4.4� Geographical coverage of traffic models 21�

5 Emission modelling considerations 24�


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5.1� Emission processes and pollutants 24�

5.2� Vehicle classification 24�

5.3� Air quality criteria 28�

5.4� Vehicle emission models 28�

5.5� Use of traffic activity data from assignment models 28�5.5.1� Compatibility issues 28�5.5.2� Traffic scaling factors 30�5.5.3� Vehicle fleet 31�

5.6� Use of traffic activity data from micro-simulation models 31�5.6.1� Compatibility issues 31�5.6.2� Traffic scaling factors 32�

5.7� Other considerations 32�

5.8� Modelling congestion 33�5.8.1� Background 33�5.8.2� Treatment of congestion in traffic models 33�5.8.3� Treatment of congestion in emission models 35�

5.9� Emission models currently used in Oxford 35�

6 Summary and recommendations 37�

6.1� Monitoring and reporting obligations 37�

6.2� Transport modes 37�

6.3� Potential applications 37�

6.4� Geographical extent 37�

6.5� Road network and spatial resolution 37�

6.6� Modelled years 39�

6.7� Temporal resolution 39�

6.8� Software and data manipulation 39�

6.9� Quality assurance 39�

6.10� Traffic model selection 39�

6.11� Emission processes and pollutants 39�

6.12� Emission model selection 40�6.12.1� Hot exhaust emissions 40�6.12.2� Cold-start emissions 41�6.12.3� Evaporative emissions 41�6.12.4� Non-exhaust emissions 41�

6.13� Vehicle classification and fleet model 41�

6.14� Modelling congestion 41�

6.15� Model integration 42�

7 Options for the city-wide model 44�

7.1� Model framework 44�7.1.1� Low-cost options 44�7.1.2� Medium-cost options 46�7.1.3� High-cost options 46�

7.2� Additional modules 47�


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7.3� Proposed development path 48�

8 Acknowledgements 50�

9 References 51�

Appendix A� Glossary of terms and abbreviations 56�

Appendix B� Emission models for road transport 58�


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Executive summary

The centre of Oxford was declared as an Air Quality Management Area (AQMA) in 2001. Subsequently, an Air Quality Action Plan (AQAP) was jointly developed by Oxford City Council and Oxfordshire County Council. One result of the city-centre AQAP was the announcement of a low-emission zone (LEZ) for buses and coaches in central Oxford. More recently, various air pollution ‘hot spots’ have been identified around the city, and this has led to the declaration of the whole of Oxford as an AQMA, requiring a further city-wide AQAP. Oxford is also currently developing a city-wide ‘Low-Emission Strategy’ to consider further options for integrating local policies, particularly those relating to transport planning and air quality.

The evidence suggests that reductions in emissions will be required for compliance with air quality objectives. Road traffic is an important source of air pollution in the city, and is therefore being targeted. Moreover, the Council is concerned that air pollution models are underestimating concentrations, and that deficiencies in the modelling of traffic and emissions are partly responsible. The more detailed and accurate estimation of emissions from road traffic (and also ambient concentrations) is necessary for the formulation of effective policies and measures for reducing air pollution in Oxford.

A number of road traffic and emission models are currently being used in Oxford. For example, Oxford City Council has recently developed an emission inventory model for road traffic in the city which uses data from a traffic assignment model (SATURN) and takes into account emissions from congested traffic. A traffic micro-simulation approach (VISSIM) is also being used in preference to SATURN at some locations - such as the LEZ in the city centre and for a scheme on Botley Road - to give a more detailed representation of vehicle operation.

To support the city-wide AQAP, Oxford City Council and Oxfordshire County Council are now keen to develop a city-wide emissions model for road traffic which consolidates the various modelling activities. In addition, the Council is concerned that the existing models may not provide the level of detail and sophistication required for the specific types of assessment it wishes to undertake, and so further model development may be required. The City Council has commissioned TRL to define the scope of the city-wide model, and to provide some recommendations to aid its development.

The main objectives of the model included the establishment of a traffic emissions baseline, the assessment of traffic emissions in relation to local air quality and climate change targets, the assessment of the impacts of future scenarios, highlighting how proposed policies and measures affect emissions, the use of data from the traffic models that have been developed for the city, and improved modelling of emissions from queuing and congested traffic.

This Report defines the scope of the Oxford city-wide model in terms of the monitoring and reporting obligations for local authorities which relate to road traffic and/or air pollution, general modelling considerations, considerations which relate specifically to traffic modelling and criteria which relate specifically to the modelling of vehicle emissions and air pollution. The scoping study has identified traffic and emission models which are available, and how they could be used to meet the Council’s objectives. Air quality (dispersion) models have not been considered.

The various considerations are summarised in the context of the likely requirements of the city-wide model, and some recommendations are provided. Options for model development, with indicative costs, are then presented.


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1 Introduction

1.1 Background and objectives

The centre of Oxford was declared as an Air Quality Management Area (AQMA) in 2001, when predictions indicated that the annual mean concentration of nitrogen dioxide (NO2)would not comply with the limit value of 40 µg/m3 by the target date of December 2005. Subsequently, an Air Quality Action Plan (AQAP) was jointly developed by Oxford City Council and Oxfordshire County Council (Oxford City Council, 2006). One result of the city-centre AQAP was the announcement in April 2009 of a low-emission zone (LEZ) for buses and coaches in central Oxford. The LEZ will operate from 2014 onwards.

More recently, various air pollution ‘hot spots’ have been identified around the city, and this has led to the declaration of the whole of Oxford as an AQMA, requiring a further city-wide AQAP. Oxford is also currently developing a city-wide ‘Low-Emission Strategy’ to consider further options for integrating local policies, particularly those relating to transport planning and air quality (Oxford City Council, 2009).

The evidence suggests that reductions in emissions will be required for compliance with air quality objectives. Road traffic is an important source of air pollution in the city, and is therefore being targeted. According to Oxford City Council (2009), on average up to 60-65% of the total emissions of nitrogen oxides (NOx) in the city-centre AQMA are from road traffic, and within particular streets the proportion may be even higher. Moreover, the Council is concerned that air pollution models are underestimating concentrations, and that deficiencies in the modelling of traffic and emissions are partly responsible. The more detailed and accurate estimation of emissions from road traffic (and also ambient concentrations) is necessary for the formulation of effective policies and measures for reducing air pollution in Oxford.

A number of road traffic and emission models are currently being used in Oxford. For example, Oxford City Council has recently developed an emission inventory model for road traffic in the city which uses data from a traffic assignment model (SATURN1) and takes into account emissions from congested traffic. A traffic micro-simulation approach (VISSIM2) is also being used in preference to SATURN at some locations - such as the LEZ in the city centre and for a scheme on Botley Road - to give a more detailed representation of vehicle operation. To support the city-wide AQAP, Oxford City Council and Oxfordshire County Council are now keen to develop a city-wide emissions model for road traffic which consolidates the various modelling activities. In addition, the Council is concerned that the existing models may not provide the level of detail and sophistication required for the specific types of assessment it wishes to undertake, and so further model development may be required.

The City Council has commissioned TRL to define the scope of the city-wide model, and to provide some recommendations to aid its development. The main objectives of the model are as follows:

(1) To establish a city-wide traffic emissions baseline, with the disaggregation of emissions by vehicle type (e.g. car, LGV, bus, HGV, etc.) and by location (i.e.street). The baseline will need to be updated to take account of new schemes and changes in traffic patterns throughout the lifetime of the next Local Transport Plan (2011-2030).

1 SATURN = Simulation and Assignment of Traffic to Urban Road Networks. http://www.saturnsoftware.co.uk/index.html 2 VISSIM is a German acronym for ‘Traffic in Towns – Simulation’) http://www.english.ptv.de/cgi-bin/traffic/traf_vissim.pl


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(2) To continue the assessment of traffic emissions in relation to local air quality and climate change targets, as defined within the UK Air Quality Strategy, Local Transport Plans, Local Strategic Partnerships and National Indicators.

(3) To assess the impacts of future scenarios, highlighting how proposed policies and measures affect emissions.

(4) To incorporate a methodology which uses data from the traffic models that have been developed for the city to predict changes in emissions arising from future scenarios. In simple terms, this will involve testing scenarios with the appropriate traffic model(s) and then using those traffic model outputs to predict changes in emissions.

(5) To include the modelling of emissions from queuing and congested traffic.

The City Council specified that the city-wide model should also have the following attributes:

• It should allow the Council to easily identify actions to reduce emissions.

• It should allow the Council to understand the effects of congestion on emissions.

• Is should allow the easy comparison of options.

• It could be used by Council officers to minimise future reliance on consultants.

Whilst these attributes are desirable, it is worth emphasising that the effects of traffic-related measures and policies will be evaluated principally using the outputs from the traffic models. Given that emission model developers have little control over the traffic models being used in Oxford, there are likely to be constraints upon extent to which some of the requirements can be addressed in the emission model itself.

1.2 Approach and report structure

Many different issues are associated with the modelling of road traffic and emissions. These issues are considered systematically in this Report.

The scope of the Oxford city-wide model is defined in terms of the following:

• The monitoring and reporting obligations for local authorities which relate to road traffic and/or air pollution, including Local Air Quality Management, Local Transport Plans, annual reporting to DfT, and indicators of sustainable development. These requirements, which influence the model specification, are summarised in Chapter 2 of the report.

• General modelling considerations which, whilst relevant to both emission and traffic modelling, are not restricted to either. These include the scenarios to be evaluated, the geographical boundary of the modelling domain, the temporal and spatial resolution of the calculations, the software platform, etc. These general considerations are addressed in Chapter 3 of the Report.

• Considerations which relate specifically to traffic modelling. These are described in Chapter 4 but, as noted earlier, they are often beyond the control of the emission model developer. The traffic models currently being used in Oxford are also summarised in Chapter 4.

• The criteria which relate specifically to the modelling of vehicle emissions and air pollution (pollutants, vehicle types, emission standards, etc.). These are described in Chapter 5.

The scoping study has identified traffic and emission models which are available, and how they could be used to meet the Council’s objectives. Air quality (dispersion) models have not been considered.


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In Chapter 6 the various considerations are summarised in the context of the likely requirements of the city-wide model, and some recommendations are provided. Options for model development, with indicative costs, are then presented in Chapter 7.

A similar approach has been used by TRL to develop a traffic and emission model (TEEM3) from the West London Alliance, and this study draws extensively from the reports produced during that work (e.g. Emmerson et al., 2006; Boulter et al., 2007).

3 TEEM = Traffic and Enhanced Emission Model


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2 Monitoring and reporting obligations relating to road traffic and/or air pollution

2.1 Overview

The scope and content of the model, and in particular the outputs, should be consistent with the obligations which local authorities have in terms of the monitoring and reporting of emissions and air pollution. These obligations are summarised in the following Sections.

2.2 Local Air Quality Management (LAQM)

Under the UK Local Air Quality Management (LAQM) framework, introduced under Part IV of the Environment Act 1995, local authorities are required to assess concentrations of specified air pollutants against the objectives in the Air Quality Regulations. The objectives are also listed in the Air Quality Strategy (AQS) for England, Scotland, Wales and Northern Ireland (DEFRA, 2007). A summary of the pollutants contained in the AQS, including the relevant metrics and compliance dates, is presented in Table 1.

Table 1: Summary of Air Quality Strategy Objectives (DEFRA, 2009).

Pollutant Concentration Measured as Compliance date

Benzene 16.25 µg/m3 Running annual mean 31/12/2003

5.00 µg/m3 Annual mean 31/12/2010

1,3-butadiene 2.25 µg/m3 Running annual mean 31/12/2003

Carbon monoxide 10.0 µg/m3 Maximum daily running 8-hour mean

31/12/2003

Lead 0.50 µg/m3 Annual mean 31/12/2004

0.25 µg/m3 Annual mean 31/12/2008

Nitrogen dioxide 200 µg/m3, not to be exceeded more than 18 times a year

One-hour mean 31/12/2005

40 µg/m3 Annual mean 31/12/2005

Particles (PM10)(gravimetric)a

50 µg/m3 , not to be exceeded more than 35 times a year

24-hour mean 31/12/2004

40 µg/m3 Annual mean 31/12/2004

Sulphur dioxide 350 µg/m3, not to be exceeded more than 24 times a year

One-hour mean 31.12.2004

125 µg/m3, not to be exceeded more than 3 times a year

24-hour mean 31.12.2004

266 µg/m3, not to be exceeded more than 35 times a year

15-minute mean 31.12.2005

a Measured using the European gravimetric transfer sampler or equivalent.


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The Regulations make it clear that likely exceedences of the objectives should be assessed in relation to ‘the quality of the air at locations which are situated outside of buildings or other natural or man-made structures, above or below ground, and where members of the public are regularly present’. LAQM therefore focuses on locations where members of the public are likely to be regularly present and are likely to be exposed for a period of time appropriate to the averaging period of the objective. Authorities should not consider exceedences of the objectives at any location where relevant public exposure would not be realistic (DEFRA, 2009).

The 2007 revision of the AQS introduced new objectives for PM2.5, which is a particle metric more closely associated with adverse health effects than PM10. The PM2.5

objectives focus on reducing population exposure in urban areas, based on the concept that greater benefits can be obtained from this than from reducing exposure at hot spots. A provisional annual mean objective of 25 µg/m3 (to be achieved by 2020) will apply to the whole of the UK, with the exception of Scotland, for which the objective is 12 µg/m3. The AQS also requires a reduction of 15% in urban background concentrations across the UK between 2010 and 2020. However, the PM2.5 objectives have not been incorporated into LAQM Regulations, and hence local authorities have no statutory obligations (DEFRA, 2009). In practice it is expected that the annual mean objective for PM2.5 of 25 g/m3 will be achieved if the 24-hour objective for PM10 is met.

2.3 National indicator for air quality

The national air quality performance indicator NI 194 (Air Quality - % reduction in NOx

and primary PM10 emissions through local authority�s estate and operations) relates to emissions of NOx and PM10 across the area of the local authority (not just hot spots designated as AQMAs), and encompasses emissions from all local authority functions (meaning operations carried out by district councils, borough councils, county councils and single-tier authorities) and outsourced services (DEFRA, 2008a). The air quality performance indicator should not be used to replace statutory requirements for local air quality management (which local authorities should continue to deliver).

Monitoring for the air quality performance indicator will require local authorities to calculate their NOx and PM10 emissions through analysis of all energy/fuel bills (and those of outsourced services), using the emissions tool available from DEFRA4. Base year data are required in order for the local authority to report against the indicator, and should cover one financial year (1 April to 31 March) (DEFRA, 2008a). Where air quality is being considered as an improvement target, negotiations should be held with Local Strategic Partnerships to ensure the target is as broad and effective as possible (DEFRA, 2008a). More detailed requirements and guidance on calculation are provided by DEFRA.

2.4 National indicators for climate change

NI 185 (Percentage CO2 reduction from local authority operations (compulsory)) requires authorities to calculate the carbon emissions of their buildings and services on a yearly basis and report the results to DEFRA (08/09 was the first year of reporting). Data must be collected on the energy and fuel use in all council buildings and transport used to deliver services, including where those services have been outsourced to another contractor (DEFRA, 2008b). Again, a spreadsheet tool to help authorities calculate emissions for NI 185 is available from the DEFRA web site5. The data required for transport include the following:

• A brief outline of the transport mode (e.g. petrol or diesel car, rail, air, HGV etc).

• Journey type (fleet or business travel excluding employee commuting).

4 www.defra.gov.uk/environment/airquality/local/indicator.htm. 5 http://www.defra.gov.uk/corporate/about/what/localgovindicators/ni185.htm#corrected


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• Distance travelled (km) or fuel used (litres).

• Year of registration of vehicle.

NI 186 (Per capita reduction in CO2 emissions in the LA area (compulsory)) requires authorities to raise awareness and support carbon emission reduction strategies for the local area. NI 186 is measured using centrally produced statistics relating to end user6

CO2 emissions in the local area from business and public sector, domestic housing and road transport. The percentage reduction in CO2 per capita in each local authority area must be reported annually, with 2005 as the baseline (Department for Communities and Local Government, 2008).

The national indicator NI 188 (Planning to adapt to climate change (non-compulsory))measures progress on assessing and managing climate risks and opportunities and incorporating appropriate action into local authority and partners’ strategic planning (LRAP, 2008). Local authorities which have signed up to NI 188 will report the level of preparedness reached (graded 0 to 4), with a higher number representing further progress made in planning to adapt to climate change (LRAP, 2008). Levels 0 to 4 indicate the following degrees of preparedness:

• Level 0: Getting started

• Level 1: Public commitment and impacts assessment

• Level 2: Comprehensive risk assessment

• Level 3: Comprehensive action plan

• Level 4: Implementation, monitoring and continuous review

NI 188 is not ‘outcome-based’, as are most national indicators. Instead, it is a ‘process-based’ indicator. Local authorities are asked to undertake a risk assessment across their local area to determine the priorities for targeting adaptation activities. Local partners are then asked to implement appropriate adaptive measures (LRAP, 2008).

2.5 Local Transport Plans (LTPs)

2.5.1 Background

The Transport Act 2000 requires most local transport authorities (county councils, unitary authorities and partnerships in metropolitan areas) in England (not London) to produce and maintain an LTP. LTPs set out an authority's local transport strategies and policies, and describe an implementation programme.

In its guidance the government has laid out five goals which local authorities are expected to consider as over-arching priorities for their LTPs. The five goals are:

• To support national economic competitiveness and growth, by delivering reliable and efficient transport networks.

• To reduce transport’s emissions of carbon dioxide and other greenhouse gases, with the desired outcome of tackling climate change.

• To contribute to better safety, security and health and longer life expectancy by reducing the risk of death, injury or illness arising from transport, and by promoting travel modes that are beneficial to health.

• To promote greater equality of opportunity for all citizens, with the desired outcome of achieving a fairer society.

• To improve quality of life for transport users and non-transport users, and to promote a healthy natural environment.

6 Calculations allocate emissions from fuel producers to fuel users.


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2.5.2 The Oxfordshire County Council LTPs

The first five-year LTPs were submitted in 2000, covering the period from 2001/02 to 2005/06. The second LTPs, covering the period 2006/7 to 2010/11 were submitted in 2006. Oxfordshire County Council is currently developing its third LTP. This will be a longer-term document, covering the period 2011 to 2030, and will be accompanied by a shorter-term improvements programme. This follows amendments to the Transport Act 2008 which allowed greater flexibility in the way LTPs are developed.

The Oxfordshire LTPs focus on five priority areas:

• Tackling congestion

• Delivering accessibility

• Safer roads

• Better air quality

• Improving the street environment

The third LTP will support the County Council’s Sustainable Community Strategy, Oxfordshire 2030, and will provide the policy and strategy context for major projects in Oxford.

2.5.3 Monitoring of LTPs

One of the main applications of the city-wide emission model will be the monitoring of the impact of transport schemes in relation to the LTP objectives, including air quality, CO2, and total greenhouse gas emissions from transport in the city.

The monitoring of LTPs is necessary for the following (DfT, 2006a):

• Reporting progress to Government.

• Performance management.

• Informing future programme planning.

• Making the case for transport investment internally and to external stakeholders.

The Government expects that LTPs will show how local authorities are going to contribute to the national commitment to reducing UK greenhouse gas emissions by at least 80% on 1990 levels by 2050. LTPs also need to consider other impacts of travel on the environment, including noise, air quality, visual impact and land take.

Authorities with LTPs are required to report on up to seventeen mandatory indicators. Eight of these are specific to LTPs, but only one of the mandatory indicators (LTP8 – Air Quality) is of direct relevance to this study. This indicator describes pollutant concentrations within AQMAs7.

LTP8 is mandatory for authorities containing designated AQMAs, except where they are not related to road transport or are solely related to trunk roads. LTP8 needs to be monitored separately within each district which has designated a relevant AQMA, where the plan area includes more than one district designating an AQMA. LTP8 can be monitored for each AQMA within a designating district.

The selection of pollutant should cover the pollutant or pollutants that triggered the designation (e.g. NO2, PM10). Authorities should set out a 2004/05 baseline and a 2010/11 target relating to pollutant concentrations.

However, according to DfT there is no suitable methodology for the annual assessment of pollutant concentrations. Local authorities are therefore recommended to measure

7 As noted in the introduction, the whole of Oxford is shortly to be declared as an Air Quality Management Area. In Oxford the city-wide AQAP will be integrated into the next LTP.


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annual progress against ‘intermediate outcomes’. Appropriate intermediate outcomes may include:

• Total road transport emissions within the AQMA or area of exceedence.

• Vehicle mileage in the AQMA or area of exceedence.

• Traffic flows in the AQMA, or at key points.

Appropriate methodologies for vehicle mileage and traffic flows, including those consistent with other LTP indicators, should be adopted. Estimating total road transport emissions in an area is more complex and is likely to require the use of road traffic data and speed-related emission factors. Forecasting will also involve not only forecasting traffic levels, but also modelling other variables including changes to the composition of the vehicle fleet. Information about the estimation of road transport emissions nationally is in the National Atmospheric Emissions Inventory (NAEI).

2.6 Assessing sustainable development

To support the UK Government Sustainable Development Strategy there is a suite of 68 national indicators (HM Government, 2005). Some of the indicators which are relevant to this work are listed in Table 2. Here, there is considerable overlap (or direct equivalence) with other obligations (e.g. LAQM).

Table 2: Indicators in UK Government Sustainable Development Strategy (HM Government, 2005).

Sustainable Development Strategy indicator

Related Public Service Agreements (PSA)and other relevant policy statements

1. Greenhouse gas emissions: Kyoto target and CO2

emissions.

DEFRA PSA 2, DTI PSA 4, DfT PSA 8: To reduce greenhouse gas emissions to 12.5% below 1990 levels in line with Kyoto commitment, and move towards a 20% reduction in CO2

emissions below 1990 levels by 2010, through measures including energy efficiency and renewables.

2. CO2 emissions by end user:industry, domestic, transport (excluding international aviation), other.

3. Aviation and shipping emissions:greenhouse gases from UK-based international aviation and shipping fuel bunkers.

DfT White Papers: ‘The Future of Air Transport’ and ‘British shipping: Charting a new course’.

7. Road transport: CO2, NOx, PM10

emissions and GDP. DfT PSA 6, DEFRA PSA 8: Improve air quality by

meeting the AQS targets for CO, lead, NO2,particles, SO2, benzene and 1,3 butadiene.

DfT PSA 7, DEFRA PSA 2, DTI PSA 4: To reduce greenhouse gas emissions to 12.5% below 1990 levels in line with Kyoto commitment, and move towards a 20% reduction in CO2

emissions below 1990 levels by 2010, through measures including energy efficiency and renewables.

8. Private vehicles: CO2 emissions and car-km and household final consumption expenditure.

9. Road freight: CO2 emissions and tonne-km, tonnes and GDP.

61. Air quality and health: (a) annual levels of particles and ozone (b) days when air pollution is moderate or high

DfT PSA 6, DEFRA PSA 8: Improve air quality by meeting the AQS targets for CO, lead, NO2,particles, SO2, benzene and 1,3 butadiene


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2.7 Implications for the Oxford city-wide model

The pollutants to be included in the city-wide model, and the level of detail required when modelling emissions, are partly governed by the various local authority monitoring and reporting obligations. For several of these obligations the estimation of emission is not particularly difficult or onerous, and in some cases even surrogate statistics (‘intermediate outcomes’) may be used.

The most demanding requirement is the need to ascertain compliance with the hourly air quality standard for NO2

8. This would normally require the modelling of NOx emissions for every hour of the year. However, the UK Technical Guidance for LAQM (DEFRA, 2009) states that it is not straightforward to predict (or measure) exceedences of the 1-hour objective for NO2. By its nature, exceedences of the 1-hour objective will be highly variable from year to year, and from site to site. Dispersion models are inevitably poorer at predicting short-term peaks than they are at predicting annual mean concentrations, and the process of model verification is extremely challenging. It can therefore be assumed that exceedences of the 1-hour mean objective for NO2 are only likely to occur where the annual mean concentration is 60 µg/m3 or above. This assumption for NO2 can be used to greatly reduce the emission modelling demands in simple tools. Similarly, for PM10, DEFRA (2009) provides a relationship between the annual mean concentration and the number of exceedences of the daily limit value. However, it should be noted that there are other reasons why modelling on a one-hour time base is preferable to using annual data, and these are discussed later in the Report.

8 Non-compliance with the 15-minute or one-hour standards for SO2 is not normally a problem associated with road transport.


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3 General modelling considerations

When defining a model of this type there are a number of general considerations which, whilst relevant to both emission and traffic modelling, are not restricted to either. In this Chapter these ‘general’ considerations are discussed in the context of the development of a city-wide emission model for Oxford.

3.1 Transport modes

The city-wide emission model will only cover road transport. Consideration should be given to the modelling of the most important sources at an appropriate level of detail. For example, a Park and Ride scheme operates in Oxford, and it is recognised that bus emissions are a significant source of pollution in the city centre. The accurate characterisation of bus operations is therefore important.

3.2 Potential applications

One of the most important stages in the development of the city-wide model is the identification of the types of application for which it will be used, including the types of scenario and future plan being considered by the City and County Councils. An understanding of these is crucial, as it ensures that the traffic and emission models have the appropriate scope and functionality. The potential applications of the model were discussed at a project inception meeting, and these were subsequently defined in more detail by the Council.

The first purpose of the model will be to establish the baseline traffic emissions across the city, in relation to indicators for climate change, air quality and sustainable development.

The specific applications of the model are then likely to include the following:

(1) Assessing the effects of proposed traffic and transport schemes on emissions. A wide range of transport and highway engineering interventions is possible here, from very small-scale changes affecting just one street to global changes affecting the whole city. Examples of the kinds of scheme that may be assessed include the following:

• Relatively minor changes to traffic signal timing or junction layout.

• Major changes to junction layout (either within the Oxford ring road or on it).

• Major demand management schemes affecting traffic flows across large areas of the city, or even city-wide.

• Changes to bus routes and bus stop locations.

• Bus priority schemes.

• High-occupancy-vehicle lanes.

• Changes to speed limits.

• Changes to bus fleets.

• LEZs. Buses are a major source of emissions in Oxford, and the Council intends to declare a LEZ for buses in the city centre. The Council is also considering the extension of the LEZ to include HGVs and taxis.

In all cases it is important that the effects of traffic congestion on emissions are included. Therefore, the meaning of congestion in the context of the emission modelling must be defined.


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(2) Environmental assessments for developments where the impact on traffic emissions may be significant, including the examination of different scenarios. These could cover anything from single developments on a relatively small scale to large strategic sites comprising hundreds of new dwellings, new roads, employment, etc. For example, a number of specific developments will affect traffic flows in the city area, including those at the Westgate Centre and ‘Transform Oxford�9.

(3) Assessing combinations of transport schemes and developments - as described in (1) and (2) above - to establish the overall cumulative impacts of proposals. In the case of transport schemes this might be a strategy that includes ten or fifteen separate schemes, or the entire LTP with dozens of schemes. In the case of developments, this might be an Area Action Plan or the entire Core Strategy. The crucial point is that the tools used for emission assessment should be flexible enough to be useful for both very detailed single schemes and for broader, long-term plans and programmes.

The city-wide model will also be used to assess the effectiveness of different options for the AQAP. These options will generally be transport schemes of the types listed under (1) above.

A wide range of model parameters will be affected by schemes and developments. For example, changes to speed limits will have a significant effect on vehicle driving patterns, whereas a park-and-ride scheme could lead to a substantial re-distribution of traffic across a wide area, as well as changes in the location and magnitude of cold-start emissions. However, as noted earlier, the effects on traffic will be taken into account in the traffic model(s), and are therefore beyond the scope of the city-wide emission model itself.

3.3 Geographical extent

The model will be required to estimate emissions for the whole city of Oxford, broadly defined by the administrative area covered by the City Council. However, the precise geographical extent of the model is not defined at present, as it could be dependent upon the types of measure or policy actually being evaluated, and the boundaries will probably change with time.

3.4 Road network and spatial resolution

The road network in Oxford should be defined as precisely as possible. It is anticipated that emissions will be modelled at the road link level with geo-referencing of node points. Long roads which vary spatially in layout and traffic characteristics should be divided into shorter sections within which the road and traffic characteristics are homogeneous. The implications of this overall approach should be considered. For example, all spatial data should be generated with the intention of complying with the European Commission’s INSPIRE Directive, and therefore appropriate co-ordinate systems and terminology should be used. Emissions will also be aggregated spatially as required (e.g. total emissions in the city).

With the use of micro-simulation traffic models and modal emission models there is, intheory, the possibility of achieving a very fine spatial resolution (i.e. several metres) in

9 Transform Oxford was launched in 2008 to improve conditions for pedestrians in the city centre. The first phase of the programme involved improvements to Queen Street and the establishment of a 'Quality Bus Partnership'. The next stages are likely to include changes to the traffic bottleneck Frideswide Square near the railway station, the introduction of higher quality road paving, and improvements to High Street, St Aldate's, Broad Street, George Street and Magdalen Street.


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the traffic and emission data. However, errors in the overall modelling process currently mean that the results of such an exercise would be rather uncertain.

3.5 Modelled years

Emission and air pollution predictions are required for a number of purposes, and there is often a requirement for several different years to be modelled. In the case of Oxford the modelling does not need to cover any specific year, and therefore it seems wise to include 2009, 2010 and all future years up to, say, 2020 (with the scope to include additional years if required).

This has one main implication for traffic modelling:

• The traffic flow data in the model needs to take into account the projected future growth in traffic.

It also has two main implications for emission modelling:

• As the emission characteristics of different types of vehicle - and of vehicles complying with different emission control legislation - are rather different, and the proportions of these different types of vehicle vary according to the year, then an additional model is required to describe the evolution of the vehicle fleet with time. Local adaptations to the modelled fleet can be made where relevant data are available. This issue is addressed in more detail in Chapter 5.

• The emissions from individual vehicles - and individual types of vehicle - change with time. Factors such as the introduction of improved fuels and the development of technologies to increase vehicle efficiency will tend to lead to reductions in emissions with time. On the other hand, the deterioration of a vehicle’s emission control system with increasing age could lead to an increase in emissions with time. These factors are normally taken into account by applying adjustments to the baseline emissions functions derived from measurements. Again, this issue is addressed in Chapter 5.

3.6 Temporal resolution

The flexibility and functionality of the city-wide model could be maximised if traffic and emissions data are treated using a one-hour hour time base. The reasons for this are as follows:

• The temporal resolution of the traffic and emission models should be relevant to the metrics defined for the pollutants listed in Table 1, and for some pollutants (notably NO2)

10 there is an hourly air quality standard. It could therefore be argued that emission estimates are required for every hour of the year to enable comparisons to be made between the predicted concentrations and pollutant limit values. However, as noted earlier, some simplifications are possible.

• It is preferable, when using traffic data, to define the temporal patterns of flow, composition and speed which distinguish between weekdays, Saturdays, Sundays, bank holidays, and other days with special events, and to take seasonal effects into account. Unfortunately, such variations are not generally built into traffic models.

• Some traffic measures and policies only have an effect on certain days, or at certain times of day, or the effect may vary during the day.

However, the processing of hourly data can be time-consuming and difficult to handle in a simple spreadsheet model.

10 The hourly mean NO2 concentration should not exceed 200 µg m3 more than 18 times a year.


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3.7 Software and data manipulation

It is unlikely that the traffic model and the emission model would be integrated in a single package. The Council has stated a preference for a relatively straightforward software platform for the emission calculations which can be used with minimal future reliance on consultants. This implies that a standalone spreadsheet running in Windows might be desirable, although this is not intended to be prescriptive. However, as noted earlier, a spreadsheet model would not be ideal for processing large amounts of data.

The manipulation and presentation of the input data and output data is an important element of any model or inventory. The geo-referencing of data permits manipulation in a geographical information system (GIS), and this in turn opens up a wide range of possibilities in terms of presentation. It is also important for related work such as air pollution modelling. It is anticipated that the model would provide a simple user interface to enable data viewing and manipulation, but that GIS would be used for more complex manipulation, visualisation and analysis. The spatially referenced traffic data will probably be manipulated in a GIS and exported to the emission model for processing.

Licences and software control will need to be clarified and defined. One issue will be the future administration of updated versions of the model.

3.8 Quality assurance

To maintain an effective and consistent tool, protocols for handling data, and the associated issue of quality assurance, would need to be established.


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4 Traffic modelling considerations

4.1 Overview

There are no regulatory requirements for traffic modelling. However, traffic modelling should be undertaken for major road schemes (i.e. those costing over £5 million) in line with the criteria described in the DfT’s Transport Analysis Guidance (DfT, 2004). Traffic modelling is also usually conducted as part of an Environmental Impact Assessment (EIA)11. An EIA applies to the specific types of development described in the Guidance (ODPM, 2006). The procedure requires the developer to compile an Environmental Statement (ES), describing the likely significant effects of the development on the environment and proposed mitigation measures. Schemes that require an EIA generally fall within the following three main criteria:

(1) Major developments which are of more than local importance. (2) Developments which are proposed for environmentally sensitive or vulnerable

locations.(3) Developments with unusually complex and potentially hazardous environmental

effects.Otherwise, traffic modelling tends to be conducted on an ad hoc basis.

4.2 Modelling approaches

This Section of the Report provides a brief review of road traffic modelling approaches. Individual models are not described in great detail.

4.2.1 Junction-based traffic models

The simplest types of traffic model are those which are used to analyse individual junctions. In most cases, such analyses are undertaken using specialist models, such as the following:

• Priority junctions - PICADY (Priority Intersection Capacity and Delay)

• Roundabouts - ARCADY (Assessment of Roundabout Capacity and Delay)

• Traffic lights - OSCADY (Optimised Signal Capacity and Delay) - TRANSYT - LINSIG

PICADY, ARCADY, OSCADY and TRANSYT are produced by TRL12. LINSIG is available from JTC consultancy13. With the exception of TRANSYT, these models can estimate, for a given traffic demand and junction configuration, queuing delays and fuel consumption, but the impacts of junction changes on the network as a whole cannot be estimated.

TRANSYT is specifically designed to estimate the impacts on queuing and stopping events of changes in demand and layout for a series of linked traffic signals. The model can estimate the impacts of changes on a network, but travel patterns (i.e. the numbers of trips per time-period between an origin and a destination) and the routing of traffic through the network (i.e. the chain of road segments) are fixed. TRANSYT also provides estimates of delays, fuel consumption and emissions for the network under consideration.

11 The requirement for EIA comes from a European Directive (85/33/EEC as amended by 97/11/EC). 12 http://www.trlsoftware.co.uk/index.asp?section=Products 13 www.jctconsultancy.co.uk


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It can be difficult to apply these single junction models to complex types of junction, such as signalised roundabouts and gyratory systems, and ‘micro-simulation’ models (see Section 4.2.3) have started to be used for such applications.

4.2.2 Traffic assignment models

Where the implications of transport policies and infrastructure changes need to be analysed, it is normal to construct a ‘traffic assignment’ model to analyse changes in traffic flows, delays and emissions on the network. These models are therefore often termed ‘macroscopic’. Such models typically deal with traffic flows per hour, although the time period covered may vary from 30 minutes to 24 hours. Assignment models have become rather sophisticated, and now allow the modelling of different vehicle types and large networks with stable results.

For most scenarios, the travel pattern is fixed, but the assignment model determines the route by minimising a combination of journey time and cost (known as ‘generalised’ cost). In some cases, the assumption that the travel pattern remains fixed can be relaxed, and the actual travel patterns can be allowed to change as the travel costs on the network change. In other cases, travel demand modelling takes place in a separate model.

In some assignment models the travel demand routines are quite sophisticated, employing discreet packets of vehicles but still using the same aggregate delay relationships as junction and assignment models. These are therefore termed ‘mesoscopic’ models, and examples include the following:

• CONTRAM (CONtinuous TRaffic Assignment Model)14

• SATURN • Cube15

• EMME/216

• VISUM17

The last three models can also be used to assign public transport passengers to different modes.

4.2.3 Micro-simulation traffic models

In recent years more practical use has been made of micro-simulation traffic models, which attempt to predict the operation of individual vehicles in real time, over a series of short time intervals, using models of driver behaviour, such as car-following and gap acceptance theories, rather than aggregate relationships. Such models allow the user to assess the impacts of policies and infrastructure changes on individual types of driver, time-varying policies, and complex junctions and layouts. Modern micro-simulation models tend to have visually impressive displays of outputs, but calibration and validation can be onerous, and large amounts of data can be generated. The current state of this market is summarised in the recent TfL guide to micro-simulation modelling (TfL, 2003). The best-know examples are:

• DRACULA (Dynamic Route Assignment Combining User Learning and micro-simulation)18

• PARAMICS (PARAllel MICroscopic Simulation)19

• VISSIM

14 http://www.contram.com/news/developments.shtml 15 http://www.citilabs.com/ 16 http://www.inro.ca/en/products/emme2/index.php 17 http://www.english.ptv.de/cgi-bin/traffic/traf_visum.pl 18 http://www.saturnsoftware.co.uk/9.html 19 http://www.sias.co.uk/sias/s-paramics/paramicsmainpage.html


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4.3 Traffic models currently used in Oxford

The principal road traffic models and analytical tools used in Oxford have been developed by Halcrow Group. These are:

• Central Oxfordshire Traffic Model (COTM), based on SATURN • City centre VISSIM models • Central Oxfordshire Aimsun Model • INTRA-SIM • VIBAT

Following a request from Oxfordshire County Council, a review of the traffic modelling work being undertaken in Oxford was produced specifically for this project (Halcrow Group, 2010). This review was the principal source of information for Sections 4.3.1 to 4.3.3 below. Information on INTRA-SIM and VIBAT (sections 4.3.4 and 4.3.5 respectively was obtained from the published literature). The characteristics of COTM, the VISSIM models and the Aimsun model are summarised, with the exception of geographical coverage which treated separately in Section 4.4.

4.3.1 Central Oxfordshire Traffic Model

Overview

Oxfordshire County Council appointed Halcrow to develop a multi-modal transport model of Central Oxfordshire (the Central Oxfordshire Transport Model - COTM). The model supports the development of Access to Oxford and the Central Oxfordshire Transport Strategy. The model has highway and public transport network representations, and has been used to undertake initial tests on the Oxford City Local Development Framework.

COTM is actually a suite of models containing the following:

• A highway model (SATURN). • A public transport model (EMME/3). • A demand model (combination of EMME/3 and a spreadsheet).

Model year(s)

The base year, as defined for the purposes of the COTM, was 2007. The 2007 base year assignment model (including calibration and validation) operates as independent highway and public transport models. The future year assignments (2016, 2026) operate as variable-demand models, whereby the highway and public transport models interact with each other.

Time periods

The model covers the following time periods:

• AM peak hour (08:00-09:00)

• Inter-peak hour (average hour between 10:00 and 16:00)

• PM peak hour (17:00-18:00)

Traffic assignment

The COTM modelling framework is shown in Figure 1. The local demand model provides forecasts of demand for a given scenario via the highway and public transport (PT) assignment models. These feed travel time information back to the local demand model for modal split, destination choice and trip-end assessment.


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The functional requirements within the local demand model include:

• Trip generation and frequency. • Mode choice. • Trip (re)distribution (or destination choice). • Time of day choice. • Output matrices for assignment within the highway and public transport models.

Figure 1: COTM modelling framework (Halcrow Group, 2010).

Two transport modes have been defined within the base EMME/3 model, namely buses and trains. In addition to these modes the model has a park-and-ride sub-model to reflect the associated two-part trips.

The variable-demand function of COTM compares the cost of travel by public transport with that by car in order to decide on mode choice and route choice. COTM can conclude that the cost to undertake a trip is prohibitive, and thus the trips will not be made (the trip is ‘suppressed’). Based on the results of the comparisons of cost of travel by public transport and car, COTM can also make changes to the destinations of trips, reflecting new total journey times between origins and destinations.

Factors are used to reflect the ‘reluctance’ of people to use public transport. These take account of the way in which, for example, potential passengers perceive waiting times and the time it takes to interchange between public transport services. These time costs, when added to the journey time and fare, provide a total cost to undertake the trip by public transport.

Highway matrix building

The highway matrices were built from two primary sources:

• The roadside interview (RSI) data - used to generate what is termed the ‘observed matrix’. This provides trip movements by purpose between the COTM sectors.

• A series of distribution models - built to determine the intra-sector movements that were not observed through the RSI surveys, by purpose. This part of the matrix has been termed the ‘synthetic matrix’.

COTM PT ModelAssignment

COTM HIGHWAYModel Assignment

STRATEGY DEVELOPMENT & APPRAISALLocal micro-

simulation models

LOCAL COTM DEMAND MODELNationalTRANSPORTMODEL(Tempro)

Local PlanningData


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When combined, these elements provided a complete pattern of trips for all vehicle movements.

Roadside interview data and associated manual classified counts (MCC) were obtained from different projects for building the observed matrices for 119 sites (Table 3). Automatic traffic count (ATC) data were also used to allow typical traffic flows through an interview site to be determined over a longer period of time.

Table 3: Summary of RSI data used in highway matrix.

Area Model Number of sites

Bicester Bicester SATURN 8

Witney Witney SATURN 12

Didcot-Wantage Didcot-Wantage SATURN 19

Remainder of Central Oxfordshire COTM SATURN 39

LATS Used for SERTM 41

Total 119

Annual average daily traffic flows on roads throughout Oxfordshire were used to calculate the traffic growth between 2001 and 2007, and between 2005 and 2007 (applied to Witney, Didcot and Wantage data) in different areas. Population and household data, drawn from the 2001 Census, were used to produce synthesised trip productions and attractions across the region and country. Household survey data were used to assist the development of the synthetic matrices.

Validation

The link validation screen lines, and the locations of the counts along them, are shown in Figure 2.

Figure 2: Locations of screen lines and traffic counts (Halcrow Group, 2010).


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4.3.2 City-centre VISSIM model

Overview

Since February 2009, Halcrow has been responsible for developing VISSIM micro-simulation models of three areas within Oxford city centre:

• City centre wider-area model • Botley Road and A34/A420 sub-model • Frideswide Square sub-model

Model year(s)

All models have been built to replicate base-year traffic conditions observed in 2008.

Time periods

The following peak hours are represented in all three models:

• AM peak between 08:00 and 09:00 • PM peak between 17:00 and 18:00

In addition to the above, all AM and PM models include ‘warm-up’ periods of 1-hour and 30-minutes respectively. The purpose of these is to populate the networks with a realistic level of traffic (and delay) prior to the peak hour evaluation periods.

Traffic assignment

Due to the availability of route choice within the city centre wider-area model, traffic has been assigned ‘dynamically’ using origin/destination-based matrices. Run times for the city centre wider-area models can be lengthy with multiple overnight runs a necessity. Halcrow (2010) note that achieving ‘stability’ in heavily congested models can be extremely difficult. The Botley Road sub-models contain no route choice. Due to the nature of the schemes being tested within the Frideswide Square sub-models, it was necessary to retain a small degree of route choice.

Vehicle Types/Classes

All models include the following vehicle types/classes: cars, LGVs, HGVs and buses.

Vehicle Demand

During development of the city centre wider-area model, demand matrices representing cars, LGVs and HGVs were cordoned from the 2007 COTM and then furnessed using SATURN SATME2/SATPIJA to match observed 2008 turning movements. These matrices were then cordoned once again for use in the Botley Road and Frideswide Square sub-models. In all models, buses have been assigned statically using fixed routes. In the city centre wider-area and Botley Road sub-models, bus arrivals and dwell times are based on timetables. In the Frideswide Square sub-models, however, individual bus arrivals and dwell times have been explicitly coded based on detailed observations made at the bus stops within Frideswide Square itself.

4.3.3 Central Oxfordshire AIMSUN Model

Overview

Halcrow has also been commissioned by the County Council to produce a strategic traffic model with micro-simulation capabilities which covers the greater Oxford area. The model was initially conceived with a view to the testing and development of dynamic


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network control strategies across Oxfordshire. However, the longer-term aspiration is to link the model to Oxfordshire’s UTMC20 database. This purpose has evolved with time and the model is now also seen as a key component in appraising the impacts of the highway improvements proposed as part of the Access to Oxford scheme bid.

Based on its macro/meso/micro-simulation capabilities, the built in SATURN interface, and the ability to enhance and take control of the software mid-simulation via an API, it was decided that the Aimsun model would provide the most suitable platform. It should be noted that the Aimsun model is still in the mid to late development stages and is yet to be validated and deemed suitable for purpose.

It is important to note that a rudimentary level of detail with respect to signal control, saturation flow and bus operations has been coded within the city centre. Given the initial purpose of the model (to test wider-area network strategies within Oxfordshire), this has been deemed to be acceptable.

Whilst developing the model, Halcrow have encountered virtual memory problems when running the macro level assignment within Aimsun. Due to the size of the network, the number of zones, the degree of route choice and the amount of data Aimsun retains during and post assignment, the model as it currently stands would appear to be at its absolute maximum.

Model year(s)

In line with the validated COTM, the model has been built to replicate Base-year traffic conditions observed in 2007.

Time periods

The following peak hours are represented within the model:

• AM peak between 08:00 and 09:00 • PM peak between 17:00 and 18:00

As with the VISSIM models, the Aimsun model also requires an appropriate ‘warm-up’ period to be assigned so as not to underestimate delay within the network. However, given the size of the network currently being modelled, deciding upon an appropriate duration and profile for this is extremely difficult. This matter has been further complicated in light of the virtual memory problems experienced by Halcrow whilst running the macro level assignment. As a result, both AM and PM models include the most basic of ‘warm-up’ periods (i.e. the first 30 minutes of the peak hour demand is assigned prior to the beginning of the peak). This is acknowledged as being less than ideal, and Halcrow and the County Council are considering reducing the size of the network and/or creating more detailed sub-models of key areas.

Traffic assignment

Aimsun offers a three-tier approach to traffic assignment (macro/meso/micro). As part of the base model development, the (link-based) macro-level assignment was selected in an attempt to mirror the assigned routing in the COTM. A ‘goodness-of-fit’ comparison between the assigned Aimsun macro and COTM link flows at approximately 300 locations was then undertaken. With routing deemed to be acceptable, routes and volumes are passed over to the micro-simulation module for validation against ANPR journey times.

Vehicle types/classes

All models include the following vehicle types/classes: cars, LGVs, HGVs and Buses. However, the model currently only includes those bus services serving the four park-

20 UTMC – Urban Traffic Management and Control.


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and-ride sites within the model.

Vehicle demand

Demand matrices representing cars, LGVs and HGVs have been cordoned from the 2007 COTM for use in the Aimsun base model. Halcrow have expressed concerns over the use of ‘projected’ future year traffic flows from SATURN in micro-simulation traffic models. This is based upon the over-optimistic nature of SATURN with regards to junction capacity, in particular at un-signalised roundabouts.

4.3.4 INTRA-SIM

INTRA-SIM (Integrated Transport Appraisal and Simulation) is a transport policy tool, also developed by Halcrow. The tool simulates the strategic choices available to transport and urban planners, wider decision makers and stakeholders in terms of moving towards a sustainable travel future for Oxfordshire. The goal is to achieve an optimum balance of different objectives, including economic, climate change, local environment, accessibility, and safety indicators. Multiple policy measures can be selected, at varying levels of application, and packaged to create scenarios of the future that achieve optimised benefits. The methodology draws on multi-modal modelling, scenario testing, forecasting and backcasting approaches. The results can be explored by different geographical area, including Oxfordshire, Oxford urban area, towns, rural areas and districts.

One element of the INTRA-SIM model is a link-based emission model for all of Oxfordshire (with a more detailed road network in central Oxfordshire), although the results are aggregated geographically. Future year scenarios include potential road- and rail-based schemes within Oxfordshire although there may be scope for more detailed analysis of the Oxford City area. INTRA-SIM uses a spreadsheet model to estimate emissions of air quality pollutants and CO2 based on the outputs from the COTM. It therefore appears that there is likely to be a significant overlap between the work being conducted on INTRA-SIM and the city-wide emissions model which is the subject of this Scoping Report.

4.3.5 VIBAT

Halcrow has undertaken visioning and back-casting for transport policy (VIBAT) studies for Oxfordshire. Halcrow has compiled a list of interventions by policy package – and grouped them as ‘low’, ‘medium’ and ‘high’. ‘Low’ interventions are essentially those that are already happening (therefore are largely ‘business as usual’). ‘Medium’ interventions are those that have already been allocated funding, and ‘high’ interventions are those not yet in receipt of funding.

The VIBAT outputs are compatible with WebTAG, thus making the inputs to the LTP process compatible with other disciplines.

4.4 Geographical coverage of traffic models

The SATURN model of Central Oxfordshire consists of a detailed ‘simulation network’ which covers most of the county area, extending to just south of Banbury and Brackley in the north, Thame to the east, Chilton Downs and Cholsey to the south, and Burford to the west. The definition of the simulation network involves detailed specification of both link-based and node-based data, and consists of all A-class and B-class roads as well as other major roads. The geographical coverage of the COTM (SATURN) and VISSIM models currently (2010) being used in Oxford is shown in Figure 3. Additional detail on the VISSIM model coverage is shown in Figure 4.


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It is clear that whilst the SATURN model includes all the main roads in Oxford, a large number of minor roads (mainly residential) are not included. Although the traffic flows on these roads are likely to be relatively low, they do still need to be included in any emission inventory.

It is also clear that the coverage of VISSIM is currently quite limited geographically, although it appears to include most of the roads in the city centre which carry the largest volumes of traffic. The additional areas which might be modelled using VISSIM in the future are also indicated in Figure 3, but substantial areas of the city will still be excluded. Consequently, it would not be possible at present to base a city-wide emissions model on the VISSIM output alone.

Figure 3: Geographical coverage of SATURN and VISSIM models in Oxford.


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Figure 4: VISSIM model areas in Oxford. Wider-area model = red, green and grey; Botley Road/A34/A420 sub-model = red; Frideswide Square

sub-model = green (Halcrow Group, 2010).

The area currently covered by the Aimsun model is shown in Figure 5. As noted earlier, it may not be possible at present to extend the coverage of the Aimsun model.

Figure 5: AIMSUM model coverage (Halcrow Group, 2010).


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5 Emission modelling considerations

5.1 Emission processes and pollutants

Various atmospheric pollutants are emitted from road vehicles as a result of different processes. These processes can be summarised as follows:

Combustion: The combustion of fuel in the engine results in the emission of pollution from the vehicle exhaust. A distinction can be made between ‘hot’ emissions and ‘cold-start’ emissions. Hot emissions are those produced from the exhaust when a vehicle’s engine and emission control system are at their full operational temperatures. Cold-start emissions are those produced from the exhaust when the temperatures of the engine and emission control system are between the ambient temperature and their full operational temperatures.

Evaporation: Evaporative emissions of volatile organic compounds (VOCs) emanate from the fuel systems (tanks, injection systems and fuel lines) of petrol vehicles. Evaporative emissions from diesel vehicles are considered to be negligible due to the low volatility of diesel fuel. There are three mechanisms by which petrol evaporates: diurnal emissions, hot-soak emissions and running emissions.

Abrasion: Particulate matter is generated by tyre wear, brake wear and road surface wear.

Resuspension: Material previously deposited on the road surface (by vehicles or other sources) can be suspended (or resuspended) in the atmosphere as a result of tyre shear, vehicle-induced turbulence and the action of the wind. Clearly, not all resuspension is traffic-related.

Exhaust emissions of carbon monoxide (CO), hydrocarbons (HC), oxides of nitrogen (NOx) and particulate matter (PM) are regulated by EU Directives. A range of unregulated gaseous pollutants are also emitted in vehicle exhaust, including primary NO2 and the greenhouse gases carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O). However, with the exception of CO2, unregulated pollutants have been characterised in less detail than the regulated ones. Evaporative emissions of VOCs are also regulated by EU Directives. Abrasion and resuspension processes are not regulated.

All these sources and pollutants - at the very least the regulated exhaust pollutants and CO2 - should be considered in the model. Oxford City Council requested the inclusion of total greenhouse gas emissions. As there is no widely-accepted definition of the term ‘total greenhouse gas’ in relation to road vehicle exhaust, it is assumed that this would include methane (CH4) and nitrous oxide (N2O) in addition to CO2.

Emission estimates can be presented in different units. For example, they may be presented in g/year for road links or the network, in g/km/s by road link for air pollution modelling, in g/passenger-km for passenger transport, in g/tonne-km for freight transport, etc. It is anticipated that the basic results from any emission models will be converted into such units. This will require data or assumptions relating to vehicle occupancy (or total passenger-km) and vehicle load (or total tonne-km).

The units for greenhouse gas emissions would also have to be clearly defined, as several are in use (e.g. mass, mass as CO2 equivalent, mass as carbon equivalent).

5.2 Vehicle classification

For the purpose of emission standards and other vehicle regulations, vehicles are classified according to the categories listed in Table 4. Light goods vehicles (category N1) are further divided into three weight classes, as shown in Table 5.


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Table 4: Definition of EU vehicle categories for road vehicles.

Category Description

L Two- and three-wheel vehicles.

M Motor vehicles with at least four wheels designed for the carriage of passengers.

M1 Vehicles carrying up to 9 persons and having a GVW* ≤ 3.5 tonnes.

M2 Vehicles carrying more than 9 persons, and having a GVW ≤ 5 tonnes.

M3 Vehicles carrying more than 9 persons, and having a GVW > 5 tonnes.

N Motor vehicles with at least four wheels designed for the carriage of goods.

N1 Vehicles having a GVW ≤ 3.5 tonnes.

N2 Vehicles having a GVW between 3.5 tonnes and 12 tonnes.

N3 Vehicles having a GVW exceeding 12 tonnes.

O Trailers (including semi-trailers).

* GVW = (maximum) gross vehicle weight.

Table 5: Weight classes for vehicle category N1.

ClassReference weight (RW)

Euro 1-2 Euro 3+

I RW ≤ 1250 kg RW ≤ 1305 kg

II 1250 kg < RW ≤ 1700 kg 1305 kg < RW ≤ 1760 kg

III 1700 kg < RW 1760 kg < RW

In recognition of the contribution of vehicle exhaust emissions to air pollution, measures have been taken to reduce the quantities of pollutants being emitted. All light-duty vehicle models and heavy-duty engine models sold in the UK are subject to type approval with respect to exhaust emissions, in accordance with European Union Directives. For emission testing purposes, the distinction between ‘light-duty’ and ‘heavy-duty’ is made at 3.5 tonnes.

The exhaust emission limits for cars, which have been referred to as ‘Euro’ standards since 1992, are summarised in Table 6 (pre-Euro standards, which date back to 1970, are not shown). Similar types of legislation apply to light-duty commercial vehicles, heavy-duty diesel engines, and motorcycles.

The legislative emission standard of a vehicle has a large influence on the actual emissions, and therefore needs to be taken into account when modelling. Emission levels are also dependent upon many other parameters, including vehicle-related factors such as model, weight, fuel type, technology level and mileage, as well as operational factors such as speed, acceleration, gear selection, road gradient and ambient temperature. All emission models must take into account the various factors affecting emissions, although the manner and detail in which they do so can differ substantially.


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Table 6: European Union exhaust emission limits for passenger cars21.

Emission standard

Implementation date for type

approval

Emission limit by pollutant (g/km)

CO HC HC+NOx NOx PM

Diesel

Euro 1 July 1992 2.72 - 0.97 - 0.14

Euro 2, IDI January 1996 1.0 - 0.7 - 0.08

Euro 2, DI January 1996a 1.0 - 0.9 - 0.10

Euro 3 January 2000 0.64 - 0.56 0.50 0.05

Euro 4 January 2005 0.50 - 0.30 0.25 0.025

Euro 5 September 2009 0.50 - 0.23 0.18 0.005

Euro 6 September 2014 0.50 - 0.17 0.18 0.005

Petrol

Euro 1 July 1992 2.72 - 0.97 - -

Euro 2 January 1996 2.2 - 0.5 - -

Euro 3 January 2000 2.30 0.20 - 0.15 -

Euro 4 January 2005 1.0 0.10 - 0.08 -

Euro 5 September 2009 1.0 0.10 - 0.06 0.005b

Euro 6 September 2014 1.0 0.10 - 0.06 0.005

a - until 30 September 1999 (after this date DI engines had to meet the IDI limits) b - applicable only to vehicles using lean burn DI engines

Consequently, in emission inventories and air pollution models, traffic data are required for a large number of vehicle categories in order to reflect variation in emission behaviour. The classification of vehicle during testing has a crucial bearing on how the resulting emission data can be used in models. Systems of traffic classification vary, but they generally reflect the typical formats of available traffic data and/or emission-related criteria (e.g. Euro standards).

The structure of the emission factors currently used in the UK is shown in Figure 6. In Figure 6 the sub-division of the traffic is shown in terms of ‘levels’. In both cases the traffic is divided into three main categories: LDVs, HDVs and two-wheel vehicles. For each of these main categories, a further sub-division is required according to a number of criteria, including fuel type (e.g. petrol, diesel, LPG), engine size or weight, and compliance with emission control legislation. Not all the details are included below Level 3. The disaggregation of the traffic at Levels 3-6 is usually undertaken by emission and air pollution modellers. It should be noted that:

• The LGV categories N1(I), N1(II) and N1(III) relate to the weight bands used in the type approval legislation (see Table 5).

• The weight ranges for heavy goods vehicles, buses and coaches refer to the maximum gross vehicle weight.

It is anticipated that the basic vehicle classification used in the Oxford model will be that shown for Level 2 in Figure 6. The vehicle fleet model (to be applied to Levels 3-6) is addressed later in this Chapter.

21 http://www.dieselnet.com/standards/eu/ld.html

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5.3 Air quality criteria

Ambient concentrations of particular pollutants are assessed relative to air quality standards. The air quality standards operable in the UK are those given in European Union Directives and the UK Air Quality Strategy (Table 1).

Air pollution modelling itself is beyond the scope of this project. However, the pollutants of concern (or their precursors) need to be included in the emission modelling part of the project. This is particularly relevant for the specific traffic-related pollutants which are not already regulated at vehicle type approval, such as NO2, PM10, benzene, 1,3-butadiene and polycyclic aromatic hydrocarbons (PAHs).

5.4 Vehicle emission models

Appendix B describes the principal characteristics of road vehicle emission models, mainly with reference to those models used in the UK (and more widely in Europe). It provides an overview of the different types of model, their specifications, their applications and their limitations. The aim is to highlight appropriate modelling approaches and typical inputs for the emission estimation process.

For hot exhaust emissions, the principal difference between different types of model is usually the way in which vehicle operation is represented. Where traffic activity data are obtained from a traffic assignment model, it is likely that an average-speed emission modelling approach would be used. The emission factors currently used in most applications and models in the UK (such as EMIT/ADMS, the Emission Factor Toolkit and the DMRB Screening Method) are based on the average-speed approach.

Where the traffic activity data are obtained from a micro-simulation model, a modal emission model can be used. The issues associated with these approaches are discussed in the following Sections, with reference only to hot exhaust emissions; the inputs required for estimating cold-start emissions and evaporative emissions are not produced by traffic models, although non-exhaust PM could be estimated with some adaptation of the traffic model outputs.

5.5 Use of traffic activity data from assignment models

5.5.1 Compatibility issues

It is anticipated that some of the required traffic activity data in Oxford will be supplied by the SATURN model. However, there are a number of difficulties associated with using the outputs from a traffic assignment model as inputs to an emission model. The outputs of the former are not usually defined in a manner which is ideal for use in the latter. The main differences between traffic and emission models relate to the system of road classification which is used, the time periods which are covered and, within these time periods, the ways in which traffic composition and vehicle operation are described (Boulter et al., 2008).

Road classification

Road types tend not to be defined explicitly in traffic assignment models. The distinction made with regards to road type in some types of emission model is typically between ‘urban’, ‘rural’ and ‘motorway’, which is rather ambiguous. This does not usually represent a serious problem as, if needed, road type can be assumed or inferred. Nevertheless, slightly different traffic scaling factors (see Section 5.5.2) would be required for different types of road/link. An appropriate road classification system must therefore be developed. The geo-referencing of the road network, and the manipulation of spatial data in GIS, aid the process of assigning link attributes.


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Data for minor roads are often unavailable from traffic assignment models, and therefore must be treated separately when modelling emissions.

Time periods

The time period being covered is one of the principal differences between traffic assignment models and emission models. Because many road schemes are designed for times of maximum travel demand, it is conventional practice to model hourly average traffic flows for the following time periods during an ‘average weekday’ of a ‘neutral month’:

• The morning peak period (07:00-10:00) • The inter-peak period (10:00-16:00) • The evening peak period (16:00-19:00) • Night-time

The ‘average weekday’ actually relates to four days (Monday to Thursday), and the ‘neutral month’ is usually October. Outputs are not usually provided specifically for Fridays, Saturdays and Sundays, or for minor roads.

However, in air pollution modelling exercises traffic data are required on an hourly basis for the whole year to enable comparisons to be made between the predicted concentrations and pollutant limit values22. It is also preferable, when defining the traffic data, to distinguish between weekdays, Saturdays, Sundays, bank holidays, and other days with special events, and to take seasonal effects into account. Furthermore, for emission inventory calculations all roads need to be included.

Vehicle classification

In emission models traffic data are required for large number of vehicle categories (see Figure 6), whereas traffic simulation models tend to deal with far fewer vehicle categories. An appropriate mapping is therefore required between the categories defined in the two types of model.

Traffic assignment models do not generally estimate traffic flow as a detailed function of vehicle type. The separation of the traffic into different types of vehicle is typically achieved by applying compositional information for specified road types (derived from traffic counts) to the modelled flows. Where traffic models do provide information on traffic composition, it is usually only given for a relatively small number of vehicle categories, such as cars, light goods vehicles (LGVs), heavy goods vehicles (HGVs), buses and motorcycles. In some cases, the traffic model may only provide data for ‘passenger car units’ (PCUs), or for light-duty vehicles (LDVs) and heavy-duty vehicles (HDVs). To reduce uncertainties in the overall emission estimates, the traffic model needs to maximise the detail in the compositional data. Buses and taxis are significant contributors to air pollution but are often poorly characterised in traffic models.

Vehicle operation

Vehicle operation is an important determinant of emissions, and the most common independent variable in this respect is vehicle speed. Traffic speed is also a common output of traffic assignment models. However, it is important to understand the relationship between the definition of the word ‘speed’ in emission models, and the definition used in traffic models.

Emissions factors are often stated as a function of average trip speed, and are usually available for the detailed vehicle types shown in Figure 6. In practice, the average speed along a road link is usually used as an emission model input (often this is simply the speed limit), and there is an assumed equivalence between a ‘link’ and a ‘trip’. According

22 For example, the hourly mean NO2 concentration should not exceed 200 µg m3 more than 18 times a year.


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to DfT (2006b), a better approach is to obtain average speeds close to junctions and average speeds for the 'free-flow' zones between junctions. However, this is still a simplification of real-world driving, and one which could give a false impression of accuracy. It is likely that any such treatment would need to take account of the different operation of vehicles at different junctions and at different times of day.

Traffic assignment models typically derive speeds (based on speed-flow relationships) for different road types. The resulting speeds therefore only apply to the specific period being modelled, such as the morning peak, but they are also often applied to the rest of the day (DfT, 2006b). For emission modelling, separate diurnal speed profiles are required for each link (and by direction of travel and by lane where possible). In some cases (e.g. a link with a bus lane), separate speeds may have to be provided for different vehicle categories.

For the newest vehicle technologies, emission modelling approaches are being developed which take into account both the average speed and ‘driving dynamics’ such as rates of acceleration or the proportion of time above a certain engine load. However, such information is not provided by traffic assignment models.

5.5.2 Traffic scaling factors

Various scaling factors are required in order to convert traffic assignment model outputs (and data for any roads not covered by the traffic model) into a format which is suitable for emission modelling. It is necessary to ensure that the variation in traffic conditions during the entire day - and for days not included in the traffic model - is taken into account.

Various scaling techniques have been developed for road traffic flow, speed and composition, including:

• COBA (COst Benefit Analysis). • TUBA (Transport Users Benefit Appraisal). • Local Air Quality Management Technical Guidance 2003. • Project for the Sustainable Development of Heathrow.

However, in air quality assessments traffic scaling factors have also often been developed ad hoc, probably owing to concerns about site-specific effects. The Oxford model would also be better served by local scaling factors which corresponded precisely to the specifications of the traffic assignment and emission models.

The following scaling factors are required:

(i) Flow factors to convert the peak and inter-peak traffic flows from SATURN, and the traffic flows for minor roads, into 24-hour diurnal profiles with a time interval of one hour. These flow scaling factors needed to take account of potential variations associated with, for example, road type, location, day of the week, month and year. Seasonal (monthly) scaling factors are also required.

(ii) Coarse traffic composition factors to convert the flow data for the main vehicle categories in SATURN into flows for the main vehicle categories required for emission modelling (Level 2 in Figure 6).

(iii) Detailed traffic composition factors (a fleet model) to further sub-divide the main vehicle categories in the emission model (e.g. by fuel use, vehicle weight/type, and age distribution) (Levels 3-6 in Figure 6). This is explored in mode detail in Section 5.5.3.

(iv) Appropriate speed factors to adjust the average traffic speeds in SATURN for peak and inter-peak periods for all time periods (and possibly different vehicle categories).


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An analytical approach used for developing these scaling factors (for West London) was described by Boulter et al. (2008). A similar approach could be applied to Oxford.

5.5.3 Vehicle fleet

In the previous Sections of the Report emission factors were described with reference to particular types of vehicle. In order to estimate emissions from traffic, the proportions of these vehicle types in the traffic stream need to be defined at both a coarse level (e.g.Levels 1-2 in Figure 6) and at a more detailed level (e.g. Levels 3-6 in Figure 6). Traffic statistics are rarely available at the detailed level, and therefore vehicle fleet models have been developed to provide the required information.

The vehicle fleet model used in the NAEI provides a national classification of vehicle type, age and fuel use in terms of vehicle-kilometres travelled, and covers the period from 2002 to 2025. The composition of the UK fleet is defined in terms of the proportion of vehicle kilometres travelled in a year by vehicles in each of the different Euro emission classes (Euro 1, 2 etc.), and also the petrol/diesel mix in the case of cars and LGVs.

Where it is considered that the national fleet model provides an inadequate description of the local vehicle fleet, adaptations can be made following observations. For example, video surveys can be conducted to determine vehicle age distributions, and it is understood that data from ANPR cameras in Oxford may be available for this purpose. However, there will clearly be no real local fleet data for future years. In addition, consideration should be given to the use of different fleet profiles in different parts of the city (e.g. to evaluate an LEZ). Again, this could complicate the city-wide emission model considerably.

Local authorities may have details of typical traffic activity on some of the roads in their areas, but in general these data will need to be supplemented by applying national statistics derived from, for example, the National Road Traffic Forecast. These may not adequately describe the local situation for purposes of accurately estimating emissions.

5.6 Use of traffic activity data from micro-simulation models

It is also possible that some of the traffic activity data in Oxford will be supplied by the VISSIM micro-simulation model. The main issues relating to the use of the output from VISSIM (or other micro-simulation models) for emission modelling are summarised below.

5.6.1 Compatibility issues

Traffic flow

For emission modelling purposes the total number of vehicles per specified period is required. As this type of information is fundamental to micro-simulation, this aspect of model integration is unlikely to present any difficulties.

Vehicle classification

The vehicle classification issues associated with micro-scale modelling are similar to those associated with any other type of emission modelling (as discussed in Section 5.5.1).

Vehicle operation

The subject of vehicle operation is one of the more complex aspects of emission modelling. In the case of micro-scale modelling there is ostensibly a reasonably close correspondence between the outputs of the traffic models (speed as a function of time


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and location) and the input requirements of emission models. It should be noted, however, that there is an oversimplification which can give a false impression of model accuracy. It is generally accepted by emission modellers that vehicle speed alone (or even a driving pattern) does not always give an accurate prediction of vehicle emissions at the local level. For example, the latest modal emission models use the engine power demand (taking into account factors such as specific vehicle characteristics23, vehicle load and road gradient) to calculate emissions (see Appendix B). Vehicle speed is an input to the calculation, but it is not the only input. However, it is usually the only operational output from traffic micro-simulation models, and is generally only available for relatively small geographical areas at present. Generic vehicle characteristics must be used for simplicity, but information on vehicle load and road gradient will need to be obtained from sources other than the traffic model.

It is worth noting that TRL has previously compared the output parameters from micro-simulation traffic models with the input parameters required for modal emission models (Boulter and McCrae, 2007). The emission models considered were MODEM, CMEM and PHEM, and the traffic models were VISSIM, PARAMICS and DRACULA. Examples of integrated of micro-simulation and modal models in the literature were also summarised (e.g. Park et al., 2001; Barth et al., 2001; Nam et al., 2003; Tate et al., 2005; Noland and Quddus, 2006).

TRL has also developed a post-processing module which takes the output from VISSIM and converts it into a format which is suitable for emission modelling using TRL’s Instantaneous Emission Model (IEM). As the parameters included in the VISSIM output files are user-defined, there is a need to ensure that sufficient data are provided. The minimum requirements for each vehicle modelled in VISSIM include link number, vehicle type, vehicle index number, time, vehicle speed and vehicle position. In addition, in the VISSIM output the data for each vehicle are not sorted, but are all presented in one file. The purpose of the post-processor is to evaluate the emissions from the modelled traffic data and to allocate the emissions to specific sections of each road link.

5.6.2 Traffic scaling factors

As all vehicles are addressed in traffic micro-simulation models, the main requirement in this context is the use of scaling factors for detailed traffic composition.

5.7 Other considerations

Consideration should also be given to the following during the development of the model:

• Emission scaling factors for different years. Scaling factors (fuels, technologies, after-treatment etc.) are required to enable emissions to be estimated in different years. This is likely to be problematic. The current average-speed emission factors in the UK must be used in conjunction with a series of emission scaling factors. However, emission scaling factors are not used in other types of emission model (e.g. modal models).

• The effects of specific engine or exhaust after-treatment technologies (e.g.hybrids, DPFs).

• The effects of vehicle occupancy or vehicle load.

• The effects of road gradient, in particular for heavy-duty vehicles.

23 These include, for example, the drag coefficient, gear ratios and rated engine power.


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5.8 Modelling congestion

5.8.1 Background

Oxford City Council considers the treatment of congestion to be an important consideration in the development of the city-wide emission model.

A low-speed journey may be travelled at a relatively steady speed, or in a stop-start fashion whereby long periods of idling may be followed by brief periods of travel at relatively high speeds. In each case the journey may be of the same duration and length, but the emissions produced could be very different. It is therefore important to clarify what is meant by ‘congestion’.

The meaning of the term congestion is generally agreed to be something like "roads are congested when there's so much traffic that they get clogged up" (Hedges, 2001). However, public perceptions of congestion vary widely, and users may accept congestion on some parts of the road network that they would find unacceptable elsewhere. For example, a perceived low speed on a motorway may be very different to a perceived low speed in an urban environment. Respondents to a survey in the UK (Hedges, 2001) defined congestion as traffic which is completely immobile or moving very slowly, or any density-related slowing of traffic. The study also suggested the following as an alternative classification scheme for congestion:

a) Expected congestion, occurring at well-known bottlenecks in the system.

b) Exceptional congestion, occurring as the result of a predictable major event. These events are known about in advance but the capacity to cope with them has not been built into the highway network.

c) Unexpected congestion, resulting from random occurrences such as accidents, breakdowns, emergency highway repairs and adverse weather conditions.

It is likely that the city-wide model will only be able to deal with the first type of congestion. It may be possible to take into account the other types of congestion at a later date using more sophisticated traffic models.

A universally-applicable quantitative definition of congestion has not been developed. However, congestion tends to be described in terms of traffic speed, journey time or traffic volume in relation to road capacity.

5.8.2 Treatment of congestion in traffic models

Most traffic models deal with congestion, but the degree of sophistication varies markedly. Most of central Oxfordshire is covered by a SATURN model. This is a well developed traffic assignment model with some demand modelling functionality. Limited areas within the city of Oxford are also covered by a set of VISSIM models. These two models treat congestion in slightly different ways.

SATURN

The SATURN traffic assignment model has two approaches to modelling congestion: (i) link-based and (ii) junction-based. SATURN generally uses link-based speed-flow models to estimate congestion from traffic flows.

The general equation is of the form:

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Where:

to is the free-flow travel time (in seconds)

C is the link capacity (in PCU/h)

B is a constant worked out by the program, and is equal to one half the time-period being modelled (numerically, B = 30*LTP where LTP is in minutes and Bis in seconds.)

The capacity of a link is defined by the modeller on the basis of the type of link (or perhaps the junction type at the down-stream end), whichever is thought to be lower, and the delay on the link can occur anywhere on the link. Traffic can change routes in response to changing congestion levels on existing routes, and the absolute level of trip-making can also be modelled to change as congestion changes.

This method of assessing congestion is always available, but there is also an additional method for estimating congestion within areas covered by a simulation network. In this part of the network, which need not be continuous, additional delays are estimated at junctions. Within the simulation area, the delay for each turning movement is estimated using gap acceptance theory. The values are still an average delay over the time-period, although they take account of cyclic behaviour resulting from traffic signal operation. However, such approach by itself only gives an estimate for the particular combination of traffic flows at a junction. In order to use such junction modelling in conjunction with re-routing, the delays at a turn are estimated at two other flow levels to create a turn-based form of the flow-delay relationship. Usually, the congestion levels are estimated over a one-hour time-interval, although SATURN can handle smaller time-periods (say, 30 minutes). If the average demand varies over the peak period then SATURN can be run for consecutive time-periods and any queues formed in one period can be dispersed in the next time-period. This could be useful in Oxford if travel demand is high during the period before the one for which the traffic model is run, or is high in the period after. Within the simulation network, links can have a link-delay relationship as well as a junction-based delay relationship, but in most urban areas this would only be used where speed-flow effects are expected on links independently of any junction-based delays. The most common examples of this would be suburban dual-carriageways such as the A34 and A40 around Oxford.

Thus, in theory, within the simulation network, which we understand covers much of Oxford, delays, and hence emissions, can be ascribed to links and junctions. Unfortunately, the estimation of emissions directly from the junction models, whilst estimated with SATURN, are based on research from the 1980s which has not been updated. The default estimation of delays would therefore be to use the detailed assignment output, where turns are independently identified, and use a time-based emissions model (that is based on emissions per unit time rather than emissions per unit distance). The new fuel consumption equations within the WebTAG Unit 3.5.6 (March 2010) are of this form. If time-based emission factors are not yet available, then the simulation network can be converted to a wholly link-based delay model by incorporating the turn-delays onto the link upstream. This can be done by SATURN in a function known as bufferising the network, but the spatial disaggregation between link congestion and junction congestion will be lost.

VISSIM

VISSIM is a full micro-simulation model which attempts to model the behaviour of each vehicle in the network over a period of time of usually much more than a peak hour. The speeds and time of vehicles are estimated constantly, so congestion can be estimated instantaneously across a network and through time. The impact of congestion on emissions can, in theory, be estimated with a high spatially resolution using an emissions model directly linked to vehicle behaviour at a given time. This complexity comes at the price of very detailed input data requirements and output data file sizes.


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There are also issues with variability, since VISSIM is essentially a model that relies on stochastic variations to work, and so multiple runs need to be carried out to obtain stable results.

Whilst VISSIM should be able to estimate the congestion levels more detailed spatially and temporally than a SATURN model, currently the re-routing behaviour of micro-simulation models is not as stable as that of standard assignment models such as SATURN. In many cases the SATURN routing is fed into the VISSIM models as fixed routes (this may happen with the Oxford models).

5.8.3 Treatment of congestion in emission models

Congestion is not generally modelled explicitly in emission models, although in models which require vehicle driving patterns to be specified by the user (e.g. modal emission models) the effects of traffic queuing are taken into account automatically.

Several attempts have been made to allow for congestion when using average-speed emission models. However, as noted earlier, there is a need to understand what the average-speed emission factors actually mean. The average speed values which are used in such models are trip-based, including the different modes of vehicle operation encountered on a trip (the terms ‘idle’, ‘acceleration’, ‘deceleration’ and ‘cruise’ are usually used). For high average speeds there are not many different ways of achieving a given speed (as the speed must be high for most of the driving time). However, at low average speeds many different ways are possible. For example, an average speed of 10 km/h could result from spending 60 seconds at idle and 60 seconds at 20 km/h, it could simply be 120 seconds at 10 km/h, or it could be 120 seconds of idling and 60 seconds at 30 km/h. There are, of course, many more possibilities. Average-speed models are statistical fits to data, and hence it is assumed that the driving cycles used to represent given average speeds are in some way ‘average’. These models are really designed for emission inventories, and so this vagueness (it is not clear what ‘average’ really means in practice here) is to some degree acceptable. For local applications the conditions may not be ‘average’ at all, but there is no simple way of taking this into account in an average-speed model, as alternative functions for non-average conditions are not defined. However, it is not logical to simply ‘add’ an emission where there is ‘congestion’. Even with congestion the traffic still has an average speed, and hence the situation should already be covered (although the representation of emissions could well be quite poor). It is possible that the average speed with congestion may be outside the speed range for which the average speed models are valid, and hence they should not be used for such conditions. Where an ‘extra emission’ is added, this is essentially an adjustment factor to disguise a basic problem with the modelling approach for congested (or, more generally, low-speed) traffic situations.

5.9 Emission models currently used in Oxford

The SATURN, VISSIM and AIMSUN models have in-built modules for calculating emissions. However, no use is currently made of these emission-modelling capabilities in Oxfordshire, as it has not been necessary for the way the models have been used. SATURN allows basic emissions statistics to be calculated for the simulation area based on average speeds and the number of stops, but for the majority of projects in the traffic flows derived by SATURN are passed to an external emissions model. For VISSIM an additional emissions module must be purchased separately, and Aimsun has built in capabilities.

An emission inventory for the city of Oxford has been compiled by AEA Technology (Peace et al., 2009). Various different LEZ scenarios were also examined through the modification of the inventory.


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The inventory covered the whole of the Oxford City Council area and included data for 11 pollutants, including NO2, NOx, N2O, PM10, CH4 and CO2. The inventory was derived using local data for road transport and 2005 NAEI data for other sectors.

The road traffic data were provided by Halcrow from the SATURN model, as well as by Oxford City Council and Oxfordshire County Council. However, detailed data were only provided for selected roads (the city centre). The SATURN model represented typical traffic flows in Oxford City Centre for 2007 and 2011. Traffic parameters were provided on a link-by-link basis as well as at each node/junction, with each direction of flow being counted separately. The traffic data were provided for four time periods: the AM peak period, the inter-peak period, the PM peak period and the 24-hour period.

The data provided for each link and each time period included the traffic flow (average number of vehicles per hour), the percentage of HGVs for the peak periods, a more detailed traffic breakdown for the 24-hour period (% taxis, % cars, % LGVs, % HGVs and % buses), and link speed (km/h). The link speed was provided for each hour, taking account of delays. Similar data were provided for each node/junction, although the parameters also included the average delay time and the average queue length (the average number of vehicles queuing during the representative time period).

Data for buses were provided by Oxford City Council. The data provided for each bus route included average scheduled run time (cumulative minutes) between each bus stop, average monthly actual run time (cumulative minutes) between each bus stop, and average monthly dwell time (the time spent idling at each stop). This information was provided for every trip on the route over a 24-hour period.

Speed data were also obtained using radar measurements at specified points along three separate roads. Average speeds were calculated for the peak time periods as well as the 24-hour average, and the data were used to estimate both delay(s) and queue length. Comparisons between the monitored speed data, the monitored bus time data and the SATURN data indicated that whilst the SATURN data appeared to reasonable for non-bus traffic, for both bus speed and queue length the modelled data were not appropriate, and therefore the measurements were used to estimate bus speeds and dwell times at stops.

The EMIT model was used to estimate emissions. The fleet in EMIT was manually adjusted to represent the bus fleet for Oxford for the various LEZ scenarios.

Another requirement of the inventory was to address congestion during peak hours. Buses and other road vehicles were included separately in order to allow for differences in speed. In the SATURN data traffic travelling at <=5 km/h was assumed to be queuing at a node/junction, and was provided as the average number of vehicles queuing per hour. Queue length was therefore calculated from the measurements at each of the survey points (i.e. average number of vehicles per hour travelling at less than 5 km/h). The mean queue lengths determined in this way were then compared with the modelled queue lengths at corresponding nodes/junctions. This method needs to be investigated further to examine whether it is suitable for application on a city-wide basis and to ensure that it does not violate the assumptions which are implicit in the EMIT emission factors (see comments on average-speed models in Section 5.5.1).


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6 Summary and recommendations

This Report has summarised the considerations which need to be borne in mind when modelling emissions from road traffic, and when using the outputs from traffic models to provide the inputs to emission models. The Report has also summarised the models currently being used in Oxford. This Chapter of the Report discusses the implications of the considerations in relation to the development of a city-wide emission model for the city, and provides recommendations for the development of the model itself.

6.1 Monitoring and reporting obligations

The pollutants to be included in the city-wide model, and the level of detail required when modelling emissions are, in part, dictated by the various local authority monitoring and reporting obligations. For several of these obligations the estimation of emission is not particularly difficult or onerous, and in some cases even surrogate statistics (‘intermediate outcomes’) may be used. The most demanding requirement is the need to ascertain compliance with the hourly air quality standard for NO2. This would normally require the modelling of NOx emissions for every hour of the year. A simplification can be made which requires only the annual mean to be modelled, but there are other reasons why hourly data are preferable to annual data (see Section 6.7).

6.2 Transport modes

The city-wide emission model will only cover road transport, with particular attention being paid to the accurate estimation of emissions from buses.

6.3 Potential applications

A number of different traffic and planning measures were mentioned in the Report. Appropriate assessment methodologies need to be identified for each of the different applications, and the city-wide model must have a functionality which permits it to handle the assessment of such measures.

A wide range of traffic and emission model parameters will be affected by the measures, and the complexity of the required assessment will vary. Some examples of the measures and policies described in the Report are listed in Table 7. The emission-related parameters which are likely to be affected by each measure or policy are also noted, and the type(s) of traffic and emission model which are best suited to the assessment are identified. However, the effects of such measures on traffic will primarily be taken into account in the traffic model(s), and are therefore beyond the scope of the city-wide emission model itself.

6.4 Geographical extent

The model will be required to estimate emissions for the whole city of Oxford, broadly defined by the administrative boundary.

6.5 Road network and spatial resolution

It is anticipated that emissions will be modelled at the road link level with geo-referencing of node points. Long roads which vary spatially in layout and traffic characteristics should be divided into shorter sections within which the road and traffic characteristics are homogeneous.

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6.6 Modelled years

The city-wide model should include 2009, 2010 and all future years up to 2020 (to be extended if required).

6.7 Temporal resolution

The accuracy, flexibility and functionality of the city-wide model could be maximised if traffic and emissions data are treated using a one-hour hour time base. However, the processing of hourly data can be time-consuming and difficult to handle in a simple spreadsheet model (see below).

6.8 Software and data manipulation

The Council has stated a preference for a relatively straightforward software platform for the emission calculations which can be used with minimal future reliance on consultants. This implies that a standalone Microsoft Excel spreadsheet running in Windows might be desirable, although this is not intended to be prescriptive. A spreadsheet model would not be ideal for processing large amounts of data.

It is anticipated that the model would provide a simple user interface to enable data viewing and manipulation, but that GIS would be used for more complex manipulation, visualisation and analysis.

Licences and software control will need to be clarified and defined. One issue will be the future administration of updated versions of the model.

6.9 Quality assurance

To maintain an effective and consistent tool, protocols for handling data, and the associated issue of quality assurance, would need to be established.

6.10 Traffic model selection

The road traffic models which are currently being used in Oxford and which are relevant to the development of the city-wide emissions model are SATURN (used in the COTM), VISSIM and Aimsun. Each of these could be used to a greater or lesser extent in the development of the city-wide emissions model, although there are advantages and disadvantages in each case. For example, the current coverage of the SATURN model is greater than that of the VISSIM model, but less detail is provided in terms of vehicle operation. The Aimsun model appears to have the potential to provide the broadest range of traffic data, in that incorporates different types of traffic model with, it is assumed, internal consistency. However, Halcrow have expressed concerns about the practical limitations of the model which could limit its usefulness for the purpose of the city-wide emission model. This clearly requires further investigation. The various traffic modelling options for the city-wide model are discussed in model detail in Chapter 7.

6.11 Emission processes and pollutants

All sources and pollutants from road traffic - at the very least the regulated exhaust pollutants and CO2 - should be considered in the model. Oxford City Council requested the inclusion of total greenhouse gas emissions. As there is no definitive definition of the term ‘total greenhouse gas’ in relation to road vehicle exhaust, it is assumed that this would include CH4 and N2O in addition to CO2.

Emission estimates should be presented using various units as far as possible, including g/passenger-km for passenger transport and g/tonne-km for freight transport. The units


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for greenhouse gas emissions would also have to be clearly defined, as several are in use (e.g. mass, mass as CO2 equivalent, mass as carbon equivalent).

6.12 Emission model selection

The selection of an appropriate emission modelling approach for use in Oxford must take into account a number of factors, some of the most important of which are listed below.

(i) The model must actually be available for use.

(ii) The cost of the model must not be excessive.

(iii) The selected emission model must be up-to-date, and based on emissions data which have been obtained relatively recently.

(iv) There must be a close correspondence is between traffic model outputs and emission model inputs.

The relevance of specific models is discussed below in relation to the emission sources for road vehicles.

6.12.1 Hot exhaust emissions

Aggregated emission factors for the regulated pollutants (CO, HC, NOx and PM), CO2 and fuel consumption are not generally used in detailed air pollution modelling exercises, as more sophisticated approached are available. However, they are generally the only means available for estimating emissions of many unregulated pollutants. Aggregated emission factors should only therefore be used in Oxford when emissions of unregulated pollutants are required, and more complex approaches do not exist.

Available average-speed models include the 2009 UK emission factors, COPERT 4 and ARTEMIS. The UK emission factors are widely used in the air quality review and assessment process, and are recommended for use in Oxford, where appropriate. The same emission factors are used in the EMIT model (as well as most other applications and models in the UK). Scaling factors must be used with the emission factors to take account of for future improvements in fuels and the deterioration of emission-control systems.

The only model using the corrected-average-speed approach appears to be the TEE model. However, given that this model is not widely available, and is based upon rather old emissions data, it is considered to be inappropriate for this work.

The traffic situation emission factors from HBEFA or ARTEMIS could be used in Oxford. However, the traffic situation modelling approach has not been extensively applied in the UK, and a number of assumptions would be required in order to relate the output from traffic models to the pre-defined traffic situations. At this stage, it appears that this would add an additional level of complexity to the overall model, but further consideration should be given to this approach.

The most developed multiple regression model is VERSIT+. VERSIT+ has also been linked with VISSIM in the EnViver model24. The practicality and cost of using EnViver in Oxford, or modifying the existing VISSIM model to run with VERSIT+ should be investigated.

In order to apply modal models, detailed and precise measurements of vehicle operation and location must be obtained. This is normally rather difficult for many model users, as such information is relatively expensive to collect. However, where micro-simulation traffic models are to be used in Oxford, consideration must be given to the use of a modal emission model, the best candidate being PHEM. Indeed, there is a reasonably

24http://www.english.ptv.de/software/transportation-planning-traffic-engineering/software-system-solutions/vissim/enviver/


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close correspondence between the outputs of VISSIM and the inputs required for PHEM, assuming that generic vehicle characteristics are used in the latter.

6.12.2 Cold-start emissions

The main practical options for modelling cold-start emissions are the routine in COPERT and the simplified ARTEMIS model. Further evaluation of these options is required. The use of the complex ARTEMIS model is probably precluded on account of the processing power and time required, and the likelihood that the necessary inputs will not be available from traffic models. However, TRL has developed a relatively simple spreadsheet version of the model which assumes worst-case conditions. The application of this model to individual road links would be difficult, but it could perhaps be used to provide an indication of the overall magnitude of cold-start emissions in the city, and hence their relative importance.

6.12.3 Evaporative emissions

The ARTEMIS model for evaporative emissions updates previous European estimation approaches, providing a greater level of detail for a wider range of vehicle categories. As such, the model provides the best approach for estimating evaporative emissions in Oxford. However, the acquisition of the relevant input parameters may represent a significant challenge.

6.12.4 Non-exhaust emissions

PM10 and PM2.5 emissions due to tyre wear, brake wear and road surface wear should be calculated in Oxford using the methodology presented in the European Environment Agency’s Emission Inventory Guidebook (EEA, 2004). The input data requirements of the Guidebook approach are not onerous, but the uncertainty on the predictions is rather large.

6.13 Vehicle classification and fleet model

The vehicle classification scheme used in the NAEI should be adopted for the city-wide model. This should make the integration of data from different sources more straightforward.

The vehicle fleet model used in the NAEI provides a national classification of vehicle type, age and fuel use in terms of vehicle-kilometres travelled, and covers the period from 2002 to 2025. However, where it is considered that the national fleet model provides an inadequate description of the local vehicle fleet, adaptations can be made following observations. For example, video surveys may be conducted to determine vehicle age distributions, and it is understood that data from ANPR cameras in Oxford may be available for this purpose. However, there will clearly be no real local fleet data for future years. In addition, consideration should be given to the use of different fleet profiles in different parts of the city (e.g. to evaluate an LEZ). Again, this could complicate the city-wide emission model considerably.

6.14 Modelling congestion

A method for addressing congestion should be included in the city-wide emission model. Two major approaches could be employed:

(i) Focusing on the SATURN model which covers nearly all the major roads around Oxford, and using the assignment model to model congestion, using multiple time-periods if it is thought that adjacent time-periods have significantly different demand. This would allow the assessment of polices that effect traffic speeds in


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general, and routing by vehicle type. If time-based emission relationships are available then emissions could be estimated by outputting via the SATURN tool SATDB, the link and turn flow and congestion details (turns are assumed to have no distance) and inputting the data into an emissions model. A disadvantage is that the link emissions cannot be ascribed to any particular point on the network.

(ii) Using the SATURN routing to set up a VISSIM model. A VISSIM model has the theoretical advantage that instantaneous delays (and emissions) can be estimated at very high spatial detail. Modelling congestion in this way allows for some polices to be assessed that are not possible with a conventional assignment model such as SATURN, such as variations in driver behaviour or vehicle performance, but this added functionality comes at the price of much more complex input data, calibration and output details. In areas where traffic is dominated by traffic lights and peak demand is relatively constant, the benefits of VISSIM over SATURN in terms of congestion estimation are likely to be limited. Where priority junctions dominate the network and when a variable demand profile occurs within the peak period, then VISSIM modelling can bring significant benefits in theory to the estimation of congestion as well as greater spatial definition.

6.15 Model integration

The main challenge in the development of the city-wide model will be the integration the existing traffic and emission modelling approaches in a single tool which can be used (and easily updated) by Oxford City Council and/or Oxfordshire County Council.

The input requirements for emission modelling are essentially fixed by the types of function used (i.e. the vehicle categories included, and the calculation method – average speed or second-by-second). The traffic model outputs are also quite limited, and so the principle issue is how to systematically transform the traffic model outputs into emission model inputs.

This is reasonably straightforward when one considers only two ‘compatible’ models, and has already been done several times already, including by the traffic model developers. Where traffic activity data are obtained from a traffic assignment model, it is likely that an average-speed emission modelling approach would be used. Both are quite ‘coarse’ in terms of vehicle operation. A series of conversion factors is usually required to convert the traffic model outputs to emission model inputs. For this, it is firstly important to understand exactly what the traffic model outputs mean, and the conversion factors need to be relevant to local conditions. In the case of traffic assignment models there are a number of compatibility issues which need to be resolved, notably the time periods being modelled, the system of vehicle classification, and the treatment of vehicle operation (speed).

Average-speed models are not ideal for assessing the effects of subtle changes in vehicle operation. Where the traffic activity data are obtained from a micro-simulation model, a modal emission model can be used. Here, the link between the traffic and emission models is more direct in principle, although considerable post-processing and sorting of the traffic data are still required.

Model integration is, however, more complex when different types of model are being used. For example, the results based on a SATURN/average-speed model combination will be different from those based on a micro-simulation/modal model combination for various reasons (e.g. differences in the traffic modelling approach, differences in the vehicle sample in the emission models…), and not just the ‘vehicle operation’ reason which is usually the reason for selecting one emission model or the other. Furthermore, the current UK average-speed emission factors must be used in conjunction with a series of emission scaling factors (for fuels, technologies, after-treatment, etc.) to enable


TRL 43 PPR473

emissions to be estimated for different years, but emission scaling factors are not used in other types of emission model25 (e.g. modal models).

25 This could be considered to represent a shortcoming of such models.


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7 Options for the city-wide model

7.1 Model framework

Several options for the development of the city-wide model framework are summarised in this Section. These include developments of both traffic and emission models, and are grouped according to indicative cost. Costs could vary substantially depending on the actual requirements.

The cost bands used are as follows:

• Low cost = £10,000 to £30,000

• Medium cost = £30,000 to £75,000

• High cost = >£75,000

Not all possibilities are included, but it is assumed that inferences could be made concerning likely cost, as well as the likely advantages and disadvantages, of alternative options from the information provided. The appropriateness of models for specific applications (i.e. types of assessment) was summarised in Table 7.

The following additional points are worth noting, as they are common to all options:

• It is assumed that emission estimates would be required for every road in the city.

• Each option relates to the setting up of the model with baseline conditions. The evaluation of different scenarios is not included.

• The emission modelling refers solely to the estimation of hot exhaust emissions and non-exhaust emissions of particulate matter. Further data and assumptions would be required to estimate cold-start and evaporative emissions.

• For roads which are not covered by traffic models, the traffic data would have to be determined from automatic or manual classified counts, or otherwise estimated. Existing data many be used where available. However, if many new traffic surveys are required this could increase the cost of development substantially.

• Regular video surveys (combined with ANPR if possible) should be undertaken to establish and periodically update the age distribution of vehicles by vehicle type.

• A disadvantage of all traffic models is the high cost associated with running them, and hence the limited amount of data which are available for different scenarios.

None of the options relate to the further development of the Aimsun model, as it appears that this model is currently at its limit of application and has not been sufficiently developed for use in the context of this study.

7.1.1 Low-cost options

Option 1: Existing SATURN and average-speed models

In this option, the output from only the existing Halcrow SATURN model would be used in conjunction with an existing average speed model (e.g. UK emission factors). The existing SATURN model gives a broad city-wide coverage, but a large number of roads (generally minor ones) are not included (emissions from these roads would have to be calculated separately). The option would result in a spreadsheet or database interface between SATURN and the emission model.


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Potential advantages

• Low traffic model development cost.

• Low emission model development cost.

• Low risk.

• Harmonised traffic model data26.

• Harmonised emission model data.

Potential disadvantages

• Poor spatial treatment of emissions and congestion.

Option 2: As option 1, with existing VISSIM model

In this option the outputs from both the existing SATURN and VISSIM models would be used in conjunction with an existing average-speed emission model. Some basic post-processing of the VISSIM output would perhaps be required to generate the required average speeds. However, it seems a little wasteful to use a micro-simulation traffic model and then just use the average speed, and the advantages of traffic micro-simulation would be largely lost. It addition, the VISSIM model only covers two hours of the day, and so the benefits in terms of estimating emissions could be rather limited.


• Low emission model development cost.

• Simplicity.



• Traffic data not harmonised.

• Spatial treatment of congestion will be poor, even in VISSIM areas, as emissions would be estimated from average speed alone.

Option 3: As option 2, with existing modal emission model

Here, the output from the existing SATURN model would be used in conjunction with an existing average speed model, as in option 1. However, the output from VISSIM would also be used in conjunction with an existing modal model, to provide more detail in the existing VISSIM areas. It is assumed that the main traffic characteristics (e.g. flow, %HGV) would also be taken from SATURN for the local applications. This would almost certainly, however, lead to discontinuities in the emission predictions due to the use of different types of traffic data and internal emission data.


• Low cost of emission model development.

• Improved treatment of congestion in VISSIM areas.


• Traffic model data on partially harmonised.

• Emission model data not harmonised.

• Poor spatial treatment of congestion outside VISSIM areas.

26 It is perhaps worth noting that the harmonisation of data does not increase the accuracy of the overall model, but it does make the handling of data and the presentation of results considerably easier.


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7.1.2 Medium-cost options

Option 4: As option 3, with harmonised emission model

In this case, the existing SATURN and VISSIM models would be used to generate the traffic data. Again, it is assumed that the main traffic characteristics (e.g. flow, %HGV) would also be taken from SATURN for the local applications. Harmonised emission models could then be used in conjunction with both the average speed data from SATURN and the driving patterns from VISSIM. For this, an existing modal emission model could be used to compile new average-speed functions, and both the ‘modal’ and ‘average speed’ capabilities of this model in association with the relevant traffic models. For example, TRL’s instantaneous emission model or PHEM might be suitable for this purpose. The driving cycles used as input to the instantaneous model to develop the average-speed model would be taken from VISSIM, real-world measurements or previous work, and would be relevant to the traffic conditions in Oxford. No specific allowance would be made in the modelling for stop-start traffic outside the VISSIM areas, but it could take account of junction modelling within the SATURN model that is not covered by the existing VISSIM models.


• Relatively low cost and risk.

• Potentially good treatment of congestion in VISSIM areas.

• Harmonised emission data.


• Traffic data only partially harmonised.

• Poor spatial treatment of congestion outside VISSIM areas.

Option 5: Existing SATURN with a traffic situation model

The traffic situation emission factors from HBEFA or ARTEMIS could be used in Oxford, and offer certain benefits in terms of the treatment of congestion. However, the traffic situation modelling approach has not been extensively applied in the UK, and a number of assumptions would be required in order to relate the output from traffic models to the pre-defined traffic situations. This option assumes the use of the existing SATURN model for the provision of traffic data. Costs would clearly be higher were the SATURN model itself to be extended.


• No requirement for traffic micro-simulation or modal emission modelling.

• Harmonised traffic model data.


• Treatment of congestion


• Subjective element to estimation of emissions.

• Not widely used in the UK, and so work would be required to adapt the traffic model outputs.

7.1.3 High-cost options

Option 6: Extended SATURN with average-speed emissions model

A large number of roads in the city are not presently covered by the SATURN model. Assuming it is feasible and worthwhile to do so, the model could be


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extended to cover a large proportion of the roads in the city. Otherwise, this option would be similar to option 1. It would also be possible to extend the time periods modelled, and to have a finer temporal resolution, but with an increase in cost.


• Simplicity.




• Poor spatial treatment of congestion.

Option 7: Extended VISSIM and modal emission model

This option is similar to option 4. However, it would involve the extension of the VISSIM model to cover the entire A-road and B-road network of the city, in addition to the city centre and other areas already covered. It would be assumed that other routes would be sufficiently free-flowing for average speeds from SATURN to be sufficient. The cost of this option would be high, and the stability of the resulting VISSIM model may well be poor due to the low availability of demand data. Again, longer periods of time could be modelled, but with a further increase in cost.


• Limited requirement for SATURN modelling.



• Potentially good treatment of congestion


• High cost of development.

• Long run times and multiple runs required for stable results.

• Potentially unstable traffic model results.

7.2 Additional modules

The following additional modules would be beneficial to the operation of any emission calculations based on the SATURN output:

Option 8: Local scaling factors

As noted earlier, scaling factors are required to convert the SATURN output into a format suitable for emission modelling. Although such scaling factors exist from previous work, they are not specific to Oxford. In this option, local scaling factors would be derived from traffic measurements in the city. 6. Periodic video/ANPR surveys should be used to characterise the age distributions of vehicle fleets.

Option 9: Congestion module

This option would not lead to a city-wide model on its own, but a separate module which would be used in conjunction with the data from SATURN, and therefore with any of the options which involve the use of the SATURN model. This would essentially involve the development and application of a method for improving the treatment of congestion when estimating emissions (i.e. outside the VISSIM areas). A similar approach was used in the compilation of the emission inventory


TRL 48 PPR473

for the city by Peace et al. (2009). However, as noted earlier, the logic and advantages of adopting such an approach are not well documented at present. A review of existing approaches would be included.

Both options 8 and 9 would be in the low-cost category.

Options 1-7 are summarised in Table 8, including the likely timescale of development. Options 8 and 9 will be beneficial additions to any option in which SATURN is used.

7.3 Proposed development path

The development of traffic models in the city – both in terms of their technical capability and their geographical coverage – will be crucial to the form of the city-wide emission model. The adaptation of existing emission models will be relatively straightforward. The following path of development is proposed here:

1. It is anticipated that for the foreseeable future the principal traffic modelling will involve a combination of SATURN and VISSIM. The development of the modelling framework should begin with the low-cost, low-risk options, and proceed incrementally as funding and data become available.

2. Option 1 could be completed relatively quickly. This would establish the basic average-speed emission modelling approach for use with the SATURN output.

3. Option 8 (local scaling factors) and option 9 (congestion module) should both lead to improvements in the SATURN/average-speed approach at relatively low cost. However, the cost of option 8 would increase if large numbers of traffic surveys are required.

4. It is recommended that options 2 and 3 are not implemented. In the case of option 2 the advantages of using VISSIM would be largely lost, and in option 3 the emission model predictions would not be harmonised. The next main improvement in the model framework would therefore be realised in option 4. Here, both the SATURN and VISSIM outputs would be used in conjunction with harmonised emission models. To some extent the traffic data would also be harmonised, but the implications of the proposed approach need to be considered.

5. Option 5 would represent a completely different approach to that described above. As traffic situation models are not widely used in the UK, work would be required to adapt the traffic model outputs. Before this is attempted a short feasibility study would be recommended. 7. The benefits of using the EnViver model, which combines VISSIM with VERSIT+, should also be investigated.

6. The costs and benefits of extending the SATURN and VISSIM models to cover the whole city should be investigated.

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8 Acknowledgements

The work described in this report was carried out in the Emissions and Air Pollution Group within the Centre for Sustainability division of TRL. The authors are grateful to Melanie Hobson who carried out the technical review and auditing of this report.


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9 References

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André J-M and Joumard R (2005). Modelling of cold-start excess emissions from passenger cars. INRETS Report LTE0509. INRETS, Bron, France.

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Atjay D, Weilenmann M and Soltic P (2005). Towards accurate instantaneous emission models. Atmospheric Environment, Vol 39 (13). Elsevier Science.

Barlow T J (1997). The development of high speed emission factors. TRL Report PR/SE/333/97 (unpublished). Transport Research Laboratory, Crowthorne.

Barlow T J, Hickman A J and Boulter P (2001). Exhaust emission factors 2000: Database and emission factors. Project Report PR/SE/230/00. TRL Limited, Crowthorne.

Blaikley D C W, Smith A P, Feest E A and Reading A H (2001). UG219 TRAMAQ - cold start emissions. Summary report, AEAT/ENV/R/0638 Issue 1, May 2001.

Barlow T J, Hickman A J and Boulter P (2001). Exhaust emission factors 2000: Database and emission factors. Project Report PR/SE/230/00. TRL Limited, Crowthorne.

Barth M, Malcolm C and Scora G (2001). Integrating a Comprehensive Modal Emissions Model into ATMIS. Transportation Modelling Frameworks Institute of Transportation Studies. California Partners for Advanced Transit and Highways (PATH). Research Report UCB-ITS-PRR-2001-19, University of California, Berkeley.

Boulter P G (2005). A review of emission factors and models for road vehicle non-exhaust particulate matter. TRL Report PPR065. TRL Limited, Wokingham.

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Boulter P G, Barlow T, McCrae I S, Latham S, Elst D and van der Burgwal E (2005). Road traffic characteristics, driving patterns and emission factors for congested situations. Deliverable 5.2 of the European Commission 5th Framework OSCAR project. TRL Limited, Wokingham.

Boulter P G, Thorpe A J, Harrison R M and Allen A G (2006). Road vehicle non-exhaust particulate matter: final report on emission modelling. TRL Report PPR110. TRL Limited, Wokingham

Boulter P G, Turpin K and Emmerson P (2007). West London Alliance traffic and enhanced emissions model. Sub-project 2- Traffic conversion factors: Inception Report. TRL Report PPR189. TRL Limited, Wokingham.

Boulter P G, Barlow T J, Emmerson P, Mao H and Turpin K (2008). West London Traffic and Enhanced Emission Model (TEEM): Traffic scaling factors. TRL Unpublished Report UPR/IE/033/07. TRL Limited, Wokingham.

Boulter P G, Barlow T J and McCrae I S (2009). Emission factors 2009: Report 3 - exhaust emission factors for road vehicles in the United Kingdom (2009). TRL Report PPR356. TRL Limited, Wokingham.


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Coelho M C, Fariasa T L and Rouphail N M (2005). A methodology for modelling and measuring traffic and emission performance of speed control traffic signals. Atmospheric Environment, 39, pp.2367–2376. Elsevier Science.

CONCAWE (1987). An investigation into evaporative hydrocarbon emissions from European vehicles. Report No. 87/60. The Hague: CONCAWE.

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DEFRA (2007). The Air Quality Strategy for England, Scotland, Wales and Northern Ireland (Volumes I and II). Department for Environment, Food and Rural Affairs in partnership with the Scottish Executive, Welsh Assembly Government and Department of the Environment Northern Ireland. Published by The Stationery Office, London.

DEFRA (2008a). Guidance to local authorities and Government Offices on National Indicator 194. Air Quality - % reduction in NOx and primary PM10 emissions through a local authority’s estate and operations. Department for Environment, Food and Rural Affairs. http://www.defra.gov.uk/corporate/about/what/localgovindicators/documents/ni194-guidance-2008.pdf

DEFRA (2008b). Guidance to local authorities and Government Offices on National Indicator 185. Percentage CO2 reduction from local authority operations. Department for Environment, Food and Rural Affairs. http://www.defra.gov.uk/corporate/about/what/localgovindicators/documents/ni185-guidance-2008.pdf

DEFRA (2009). Local Air Quality Management - Technical Guidance LAQM.TG(09). Department for Environment, Food and Rural Affairs, London.

De Haan P and Keller M (2000). Emission factors for passenger cars: application of instantaneous emission modelling. Atmospheric Environment, Vol 34, pp. 4629-4638. Elsevier Science.

Department for Communities and Local Government (2008). National indicators for Local Authorities and Local Authority Partnerships: Handbook of Definitions. Communities and Local Government Publications, Wetherby.

DfT (2004). Transport Analysis Guidance: Major Schemes in Local Transport Plans - TAG Unit 1.4. Department for Transport, London.

DfT (2006a). Long Term Process and Impact Evaluation of the Local Transport Plan Policy. Monitoring and Reporting of LTP Outcomes. Department for Transport. http://webarchive.nationalarchives.gov.uk/+/http://www.dft.gov.uk/pgr/regional/ltp/research/ltpoutcomes.pdf

DfT (2006b). Project for the Sustainable Development of Heathrow: Air Quality Technical Report. Department for Transport. http://www.dft.gov.uk/stellent/groups/dft_aviation/documents/divisionhomepage/612123.hcsp

EEA (2009). EMEP/EEA Air Pollutant Emission Inventory Guidebook - 2009. Technical report No 6/2009. European Environment Agency EEA, Copenhagen. http://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009/


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Emmerson P, McCrae I S, Turpin K and Boulter P G (2006). West London transportation and emissions model, Phase 1: Specification. TRL Report PPR160. TRL Limited, Wokingham.

Ericsson E (2000). Variability in urban driving patterns. Transportation Research Part D 5, pp 337-354. Elsevier Science Ltd.

European Commission (1999). MEET: Methodology for calculating transport emissions and energy consumption. Office for Official Publications of the European Communities, L-2985 Luxembourg. ISBN 92-828-6785-4.

Frey H, Rouphail N, Unal A and Colyar J (2001). Emission reduction through better traffic management: an empirical evaluation based upon on-road measurements. North Carolina Department of Transportation, Report No. FHWA/NC/2002-001, USA, http://www.ncdot.org/planning/development/research/1999-08.html

Halcrow Group (2010). Oxfordshire County Council Traffic Modelling Review. April 2010. Halcrow Group Limited, Bristol.

Hammarstrom U and H Edwards (1997). COLDSTART (Draft). Swedish National Road and Research Institute (VTT), Linköpink, Sweden.

Hansen J Q, Winter M and Sorenson S C (1995). The Influence of Driving Patterns on Petrol Passenger Car Emissions. The Science of the Total Environment, Vol. 169, pp. 129-139.

Hassounah, MI and Miller EJ, (1995). Modelling air pollution from road traffic: a review. Traffic engineering and control, Vol 35 (9), pp 510-514.

Hausberger S, Wiesmayr J, Bukvarevic E, Tripold W and Brenner J (2005).Evaporative emissions of vehicles - Final Report. European Commission 5th Framework project ARTEMIS (Assessment and Reliability of Transport Emission Models and Inventory Systems). Technical University of Graz, Austria.

HM Government (2005). Securing the future- delivering UK sustainable development strategy. Published by TSO (The Stationery Office).

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Jost P, Hassel D, Webber F-J and Sonnborn (1992). Emission and fuel consumption modelling based on continuous measurements. Deliverable No. 7, DRIVE Project V1053. TUV Rhineland, Cologne.

Kean A J, Harley R A and Kendall G R (2003). Effects of vehicle speed and engine load on motor vehicle emissions. Environmental Science and Technology, Vol. 37, No. 17, pp 3739-3746.

Leung Y C and Williams D J (2000). Modelling of Motor Vehicle Fuel Consumption and Emissions Using a Power-Based Model. Environment Monitoring and Assessment, Vol. 65, pp. 21-29.

LRAP (2008). Adapting to Climate Change. Guidance notes for NI188. The Local and Regional Partnership Board. http://www.lga.gov.uk/lga/aio/1382855

Matzoros A and Van Vliet D (1992). A Model of Air Pollution from Road Traffic. Transportation Research, pp. 315-335, Vol. 26A.

MIRA (2002). VeTESS simulation procedure. September 2002. MIRA, Nuneaton, Warwickshire CV10 0TU, United Kingdom.


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Nam E K, Gierczak C A and Butler J W (2003). A comparison of real-world and modeled emissions under conditions of variable driver aggressiveness. Ford Scientific Research Laboratory, 2101 Village Road MD 3083/SRL, Dearborn, MI 48121-2053. Paper presented at Transportation Research Board 2003 Annual Meeting.

Negrenti E, (1998). The ‘corrected average speed’ approach in ENEA's TEE model: an innovative solution for the evaluation of the energetic and environmental impacts of urban transport policies. ARRB Transport Research Limited Conference, 19th, 1998, Sydney, Australia.

Noland R B and Quddus M (2006). Flow improvements and vehicle emissions: effects of trip generation and emission control technology. Transportation Research: Part D, Vol. 11, pp. 1-14.

ODPM (2006). Environmental Impact Assessment: Guidance notes. Office of the Deputy Prime Minister. Available from http://www.odpm.gov.uk/index.asp?id=1143247.

Osses M, Henriquez A and Trivino R (2002). Positive mean acceleration for the determination of traffic emissions. Paper presented at the conference ‘Transport and Air Pollution’, Graz, Austria, 19-21 June 2002.

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Rexeis M, Hausberger S, Riemersma I, Tartakovsky L, Zvirin Y and Erwin C (2005). Heavy-duty vehicle emissions. Final Report of WP 400 in ARTEMIS (Assessment and Reliability of Transport Emission Models and Inventory Systems); DGTREN Contract 1999-RD.10429; University of Technology, Graz; report no. 02/2005/Hb 20/2000 I680.

Smit R, Smokers R and Schoen E (2005). VERSIT+ LD: Development of a new emission factor model for passenger cars linking real-world emissions to driving cycle characteristics. Proceedings of 14th International Symposium on Transport and Air Pollution Graz, Austria, 1-3 June.

Sturm P J, Boulter P, de Haan P, Joumard R, Hausberger S, Hickman A J, Keller M, Niederle W, Ntziachristos L, Reiter C, Samaras Z, Schinagl G, Schweizer T and Pischinger R (1998). 'Instantaneous emission data and their use in estimating passenger car emissions'. EC MEET Project (Methodologies for Estimating Emissions from Transport), Task 1.1:Instationary vehicle emissions, Deliverable no. 6. Published by the Technical University of Graz, Institute for Internal Combustion Engines and Thermodynamics, A-8010 Graz Inffeldgasse 25, Austria, Editor Univ.-Prof. Dr. R. Pischinger.

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TfL (2003). Micro-Simulation Modelling Guidance note for TfL. July 2003. Transport for London Street Management, London.

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Zallinger M, Anh T and Hausberger S (2005). Improving an instantaneous emission model for passenger cars. Proceedings of the 14th International Conference on Transport and Air Pollution, Graz, Austria, 1-3 June 2005. Institute for Internal Combustion Engines and Thermodynamics, Graz University of Technology, Austria.


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Appendix A Glossary of terms and abbreviations

ANPR Automatic Number Plate Recognition

AQAP Air Quality Action Plan

AQMA Air Quality Management Area

AQS (UK) Air Quality Strategy

ARTEMIS Assessment and Reliability of Transport Emission Models and Inventory Systems

CH4 Methane

CMEM Comprehensive Modal Emissions Model

CO Carbon monoxide

CO2 Carbon dioxide

COPERT Computer Program to calculate Emissions from Road Transport

COTM Central Oxfordshire Traffic Model

DMRB Design Manual for Roads and Bridges

DPF Diesel Particulate Filter

EIA Environmental Impact Assessment

GIS Geographical Information System

HBEFA Handbook of emission factors

HC Hydrocarbons

HDV Heavy-duty vehicle

HGV Heavy goods vehicle

IEM Instantaneous Emission Model

INTRA-SIM Integrated Transport Appraisal and Simulation

LAQM Local Air Quality Management

LEZ Low-Emission Zone

LDV Light-duty vehicle

LGV Light goods vehicle

LPG Liquefied petroleum gas

LTP Local Transport Plan

MODEM Modelling of emissions and consumption in urban areas

NAEI National Atmospheric Emissions Inventory

N2O Nitrous oxide

NO2 Nitrogen dioxide

NOx Oxides of nitrogen

PCU Passenger cart unit

PHEM Passenger car and Heavy-duty Emission Model


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PM2.5 Particulate matter with an aerodynamic diameter of less than 2.5 µm

PM10 Particulate matter with an aerodynamic diameter of less than 10 µm

PSA Public Service Agreement

SATURN Simulation and Assignment of Traffic to Urban Road Networks

SO2 Sulphur dioxide

TEE Traffic Energy and Emissions

TEEM Traffic and Enhanced Emission Model

UROPOL Urban Road Pollution (model)

VeTESS Vehicle Transient Emissions Simulation Software

VERSIT VERkeers SITuatie Model

VIBAT Visioning and Back-casting for Transport Policy

VISSIM A German acronym for ‘Traffic in Towns – Simulation’)


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Appendix B Emission models for road transport

B.1 Hot exhaust emissions

Models for hot exhaust emissions tend to be classified according to a combination of the geographical scale of application, the generic model type, and the nature of the emission calculation approach. These different classification approaches, with examples of specific models, are summarised in Table B1 with respect to light-duty vehicles. The generic types of model are discussed in more detail in the following paragraphs, with some further explanation of the terms and acronyms used in Appendix A.

Table B1: Models for estimating hot exhaust emissions from light-duty vehicles (Boulter et al., 2005)27.

Generic type Example Type of input data

required to define vehicle operation

Typical application

Aggregated emission factors NAEI Area or road type Emission inventories,

EIA28, SEA29

Average speed COPERT, ARTEMIS,

DMRB

Average trip speed Emission inventories

Adjusted average speed TEE Average speed, congestion level

Local assessment of UTM schemes

Traffic situation HBEFA,

ARTEMIS

Road type, speed limit, level of congestion

Inventories, EIA, SEA, area- wide assessment of UTM30

Multiple linear regression VERSIT+ Driving pattern Emission inventories

Modal models ‘Simple’ UROPOL

Distribution of driving modes Local assessment of UTM schemes

Instantaneous: speed-based and unadjusted

MODEM Driving pattern

Detailed temporal and spatial analysis of emissions

Instantaneous: power-based and unadjusted

VeTESS, PHEM (HDV)

Driving pattern, gradient, vehicle-specific data

Instantaneous: power based and adjusted

PHEM (PC) Driving pattern, gradient,

vehicle-specific data

B.1.1 Aggregated emission factor models

In aggregated31 emission factor models a single emission factor is used to represent a particular type of vehicle and a general type of driving – the traditional distinction is between urban roads, rural roads and motorways. Vehicle operation is therefore only taken into account at a very rudimentary level, and the approach cannot be used to determine emissions for situations which are not explicitly defined. The emission factors are calculated as mean values of measurements on a number of vehicles over given driving cycles, and are usually stated in terms of the mass of pollutant emitted per vehicle and per unit distance (g vehicle-1 km-1) or per unit of fuel consumed (g litre-1).

27 Most of the models listed also address other types of vehicle, such as heavy goods vehicles and buses. 28 EIA = environmental impact assessment. 29 SEA = strategic environmental assessment. 30 UTM = urban traffic management. 31 Sometimes referred to as ‘bulk’ emission factors.


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Given their simplicity, aggregated emission factors are mainly used in applications on a large spatial scale, such as national and regional emissions inventories, where little detailed information on vehicle operation is required. For detailed modelling exercises more sophisticated approached are available. However, they are generally the only means available for estimating emissions of many unregulated pollutants, for which there is insufficient information to define any more detailed relationship with vehicle operation.

A substantial number of aggregated emission factors are given in the European Environment Agency’s COPERT 432 model. For example, COPERT provides emission factors for the unregulated pollutants methane (CH4), nitrous oxide (N2O) and ammonia (NH3) for urban, rural and motorway driving, and single emission factors which relate to all types of operation for heavy metals and specific organic compounds. Emission factors have also been produced for some pollutants in the ARTEMIS33 project.

B.1.2 Average-speed models

Average-speed emission functions for road vehicles are widely used to estimate hot exhaust emissions in regional and national inventories, but are also currently used in a large proportion of local air pollution prediction models. Average-speed models are based upon the principle that the average emission factor for a certain pollutant and a given type of vehicle varies according to the average speed during a trip. The emission factor is again usually stated in grammes per vehicle-kilometre (g vehicle-1 km-1). Figure B1 shows how a continuous average-speed emission function is fitted to the emission factors measured for several vehicles over a range of driving cycles, with each cycle representing a specific type of driving, including stops, starts, accelerations and decelerations.

Figure B1: Average speed emission function (red line) for NOx emissions from Euro III diesel cars <2.0 litres. The blue points show the underlying emission measurements (Barlow et al., 2001).

32 http://lat.eng.auth.gr/copert/ 33 ARTEMIS: Assessment and Reliability of Transport Emission Models and Inventory Systems. A European Fifth Framework project. www.trl.co.uk/artemis/

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

0 10 20 30 40 50 60 70 80 90 100 110 120 130

Average speed (km h-1)

NOx(gvehicle-1km

-1)

Emissions data

Average speed function


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Some examples of average-speed models include MEET (European Commission, 1999), ARTEMIS, COPERT 4 and, in the UK, the emission factors used in the NAEI and DMRB. The UK emission factors are also used in a number of other modelling tools, such as the Cambridge Environmental Research Consultants (CERC) air pollution prediction model ADMS (the emission model in ADMS is known as EMIT)34.

A number of factors have contributed the widespread use of the average-speed approach. For example, it is one of the oldest approaches, the models are comparatively easy to use, a number of models are available free of charge, and there is a reasonably close correspondence between the required model inputs and the data generally available to users. However, there are some limitations. Trips having very different vehicle operation35 characteristics (and different emission levels) can have the same average speed. Clearly, all the types of operation associated with a given average speed cannot be accounted for by the use of a single emission factor. This is a particular problem at low average speeds, for which the range of possible operational conditions associated with a given average speed is great. In addition, for modern catalyst-equipped vehicles a large proportion of the total emission during a trip can be emitted as very short, sharp peaks, often occurring during gear changes and periods of high acceleration. Average speed has therefore become a less reliable indicator of emissions. Furthermore, average speed models do not allow for detailed spatial resolution in emission predictions, and this is an important drawback in dispersion modelling.

The concept of ‘cycle dynamics’ has become useful for emission model developers to describe variations in vehicle operation for a given average speed (e.g. Sturm et al., 1998). In qualitative terms, cycle dynamics can be thought of as the ‘aggressiveness’ of driving, or the extent of ‘transient’36 operation in a driving pattern. Quantitatively, the term refers to the variation in various properties or statistical descriptors of a vehicle operation pattern. As the information available to model users and developers has tended to be speed-based, interest has inevitably focussed on parameters which describe speed variation in some way. Some of the more useful parameters appear to be relative positive acceleration (Ericsson, 2000) and positive mean acceleration (Osses etal., 2002). However, most model users have little or no straightforward means of relating to descriptors of variation in vehicle operation, and several studies have also concluded that emissions should be described in terms of engine speed, load, power, and the changes in these parameters, not just variables relating to vehicle speed (Leung and Williams, 2000; Kean et al., 2003). Nevertheless, the concept is a useful one, especially when there is a need to discuss more advanced forms of modelling than the average-speed approach.

B.1.3 �Corrected� average speed models

The TEE (Traffic Energy and Emissions) model (Negrenti, 1998) incorporates a ‘corrected average speed’ modelling approach. The model assumes that the effect of congestion on emissions at a certain average speed can be expressed by means of a ‘correction factor’ derived from average speed, green time percentage, link length, and traffic density. The emission factor for the average speed is then adjusted using the correction factor. The congestion level is used to calculate the fractions of time spent during cruising, acceleration, deceleration and idling, and the end result is a reconstructed speed profile produced by the model itself. In fact, the TEE model uses emission factors from a simple instantaneous model (MODEM – see later) to calculate emissions for each of the phases, based on the reconstructed profile. The limitations of this part of the approach are discussed in the Section on instantaneous models.

34 http://www.cerc.co.uk/35 In this Report the term ‘vehicle operation’ refers to a wide range of parameters which describe the way in which a driver controls a vehicle (e.g. average speed, maximum speed, acceleration pattern, gear-change pattern), as well as the way in which the vehicle responds (e.g. engine speed, engine load). 36 In this context, the term ‘transient’ refers to a driving cycle in which the operation of the vehicle is continuously varying, as opposed to being in a steady state.


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B.1.4 Traffic situation models

In ‘traffic situation’ models cycle average emission rates are correlated with various driving cycle parameters. These, in turn, are referenced to specific traffic situations which are known by the model user. Different traffic situations relate to conditions for which there is a specific emission problem, and for which the average speed may not be the best indicator of emissions. Traffic situation models tend to be best suited to local applications, in which emission estimates are required for individual road links, but can also be used for regional and national inventories. The user must be able to relate to the way in which the traffic situations are defined in the model. Each emission factor is associated with a particular traffic situation, characterised by the features of the section of road concerned (e.g. ‘motorway with 120 km h-1 limit’, ‘main road outside built-up area’). Speed variation (dynamics) is not usually quantified by the user, but is defined by a textual description (e.g. ‘free-flow’, ‘stop and go’) of the type of traffic situation to which an emission factor is applicable. However, asking the user to define the traffic situation using a textual description of speed variation or dynamics may lead to inconsistencies in interpretation. As with any other model, the emission factors for the various vehicle categories must then be weighted according to traffic flow and composition.

The most widely used model of this type is the Handbook of Emission Factors (HBEFA), which is used for both national inventories and local applications in Germany, Austria, Switzerland and the Netherlands. It operates on the principles outlined above. The latest version of HBEFA (version 3.1) was produced in January 201037. The model is designed specifically for use in the three countries mentioned, with the driving patterns for each traffic situation reflecting conditions in these countries. Its applicability to the UK is therefore questionable. A similar traffic situation model was also developed in the ARTEMIS project. The emission factors have been defined, but there are currently no default traffic statistics for the UK in the model.

B.1.5 Multiple linear regression models

The VERSIT+ model (Smit et al., 2005) employs a weighted-least-squares multiple regression approach to model emissions, based on tests on a large number of vehicles over more than 50 different driving cycles. Within the model, each driving cycle used is characterised by a large number of descriptive parameters (e.g. average speed, relative positive acceleration, number of stops per km) and their derivatives. For each pollutant and vehicle category (Euro I to Euro IV) a regression model is fitted to the average emission values over the various driving cycles, resulting in the determination of the descriptive variables which are the best predictors of emissions (the group of descriptors being different in each case). A weighting is also applied to each emission value, based on the number of vehicles tested over each cycle and the inter-dependence of cycle variables. The VERSIT+ model requires a driving pattern as the input, from which it calculates the same range of descriptive variables and estimates emissions based on the regression results. The physical meaning of the variables may not necessarily be known. As with the other models requiring a driving pattern as the input, the use of the model will be restricted to a comparatively small number of users, unless the inputs can be provided by a micro-simulation traffic model (as in EnViver).

B.1.6 Modal models

In modal models emission factors are allocated to the specific modes of vehicle operation encountered during a trip. In the simplest type of modal model, vehicle operation is defined in terms of a relatively small number of modes - typically idle, acceleration,

37 http://www.hbefa.net/e/index.html


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deceleration and cruise. For each of the modes the emission rate for a given vehicle category and pollutant is assumed to be fixed, and the total emission during a trip, or on a section of road, is calculated by weighting each modal emission rate by the time spent in the mode. One example is the Urban Road Pollution (UROPOL) model (Hassounah and Miller, 1995), although a similar approach has been used elsewhere (e.g. Frey et al., 2001; Hung et al., 2005; Coelho et al., 2005).

Several more detailed modal models aim to provide a more precise description of vehicle emission behaviour by relating emission rates to vehicle operation during a series of short time steps (often one second). However, several different terms (as well as modal) have been used to describe the more detailed type of model, including ‘instantaneous’, ‘micro-scale’, ‘continuous’ and ‘on-line’ (De Haan and Keller, 2000). As the term ‘instantaneous’ has been used quite widely in the literature, it will be retained for this Report.

Atjay and Weilenmann (2004) stated that the aim of instantaneous emission modelling is to map emission measurements from tests on a chassis dynamometer or an engine test bed in a neutral way. In theory, the advantages of instantaneous models include the following:

• Emissions can be calculated for any vehicle operation profile specified by the model user, and thus new emission factors can be generated without the need for further testing.

• The models inherently take into account the dynamics of driving cycles, and can therefore be used to explain some of the variability in emissions associated with given average speeds.

• The models allow emissions to be resolved spatially, and thus have the potential to lead to improvements in the prediction of air pollution.

Some instantaneous models, especially the older ones, relate fuel consumption and/or emissions to vehicle speed and acceleration during a driving cycle, typically at one-second intervals. For each combination of speed and acceleration the emission or fuel consumption rates are usually based upon the average results from the tests on number of vehicles over different driving cycles. Examples of this approach can be found in Jost et al. (1992) and Hansen et al. (1995). Other models use some description of the engine power requirement. However, it must be noted that there are a number of fundamental problems associated with instantaneous models. For example, it is extremely difficult to measure emissions on a continuous basis with a high degree of precision, and then it is not straightforward to allocate those emission values to the correct operating conditions. Atjay and Weilenmann (2004) noted that, during measurement in the laboratory, an emission signal is dynamically delayed and smoothed, and this makes it difficult to align the emissions signal with the vehicle operating conditions. Such distortions have not been fully taken into account in instantaneous models until relatively recently. For the purposes of this report, the term ‘adjusted’ is used to refer to models in which the distortion is addressed.

MODEM

The original version of MODEM38 was produced during the European Commission’s DRIVE programme. The emission factors for a particular vehicle category and pollutant are defined in the form a two-dimensional matrix, with the columns representing speed intervals (km h-1), and the rows representing the speed x acceleration intervals (m2 s-3)(Jost et al., 1992). The speed range defined in MODEM are 0 to 90 km h-1, in increments of 10 km h-1. The speed x acceleration values range from -15 to +15 m2 s-3, in increments of 5 m2 s-3. The most recent emission legislation covered in MODEM is Euro I. Given that the newest petrol and diesel vehicles on the road conform to Euro IV standards, the model is now therefore somewhat out of date.

38 MODEM = Modelling of emissions and consumption in urban areas.


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The user must enter a driving pattern which defines vehicle speed as a function of time. From the input driving pattern the program evaluates the average speed and acceleration between each pair of adjacent speed readings, and the corresponding emission factors (CO, HC, NOx and CO2) are then referenced for each vehicle category. Emissions over the entire driving pattern are calculated as the sum of the individual emission factors. An additional set of emissions factors for use in MODEM was developed later by TRL to allow the model to be used for higher speeds. A further shortcoming of the original MODEM model was the coarse resolution of the speed and acceleration bands. Consequently, a matrix with a finer resolution was also developed for the extended version of MODEM (Barlow, 1997).

The original version of MODEM was written in the C programming language and runs in DOS. It is commercially available, with a price of £1,000 for research purposes and £6,000 for commercial purposes. The price was set by the project consortium at the time of its release (more than 10 years ago). The extended version of MODEM is very similar in terms of the user requirements, although it runs in Microsoft Excel using macros written in VBA. A commercial version of the extended model has not been produced.

PHEM (HDV part)

The ARTEMIS project and the COST Action 34639 provided a great deal of insight into the emission behaviour of modern heavy-duty vehicles. One of the main aims of ARTEMIS and the COST Action was to develop a model capable of accurately simulating emission factors for all types of HDV over any driving pattern and for various vehicle loads and gradients; the latter greatly influence driving behaviour and emission levels from HDVs. The resulting tool - PHEM (Passenger car and Heavy-duty Emission Model) - estimates fuel consumption and emissions based on the instantaneous engine power demand and engine speed during a driving pattern specified by the user (Rexeis et al., 2005).

Figure B2 shows the structure of the model. The main inputs are a user-defined driving pattern and a file describing vehicle characteristics. For every second of the driving pattern PHEM calculates the actual engine power demand based upon vehicle driving resistances and transmission losses, and calculates the actual engine speed based upon transmission ratios and a gear-shift model. For a correct simulation of engine power, all driving resistances occurring during real-world operation have to be taken into consideration. The actual engine power (P) is calculated according to the equation:

P = Prolling resistance + Pair resistance + Pacceleration + Pgradient + Ptransmission losses + Pauxiliaries

The individual terms in the total power demand equation are calculated as described by Rexeis et al. (2005). The engine power and speed are then used to reference the appropriate emission (and fuel consumption) values from steady-state engine maps. The emission behaviour over transient driving patterns is then taken into consideration by ‘transient correction functions’ which adjust the second-by-second steady-state emission values according to parameters describing the dynamics of the driving pattern.

The model covers many different vehicle categories (specified according to weight bands) and levels of emission legislation (pre-Euro I to Euro V). It also includes adjustment factors for the use of specific emission-control technologies (e.g. DPFs) and alternative fuels. The HDV part of PHEM does not include adjustments for the distortion of the emissions signal during measurement. The passenger car part of the model, on the other hand, does include a signal adjustment, and is therefore described later. PHEM is commercially available from the Technical University of Graz, but the average speed and traffic situation emission factors are already included in the main ARTEMIS model.

39 http://www.cordis.lu/cost-transport/src/cost-346.htm


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Figure B2: Structure of PHEM (Rexeis et al., 2005).

VeTESS

An emissions model called VeTESS (Vehicle Transient Emissions Simulation Software) was the main Deliverable from the European Commission 5th Framework DECADE project. It is a windows-based application which is written in the object-orientated programming language Delphi 5 (MIRA, 2002). VeTESS calculates the emissions from a single vehicleduring a driving pattern defined by the model user. The driving pattern contains details of the speed of the vehicle and the road gradient over a route and, when coupled with specific information on the vehicle, forms the basis of a series of calculations to derive the engine power at every point on the route (MIRA, 2002).

The modelling technique considers only one vehicle at a time and one journey at a time. The characterisation of a single engine for the VeTESS software can take several weeks. In addition, if an engine type or its emission-control equipment is updated, the characterisation must be repeated for the new engine. This means that the simulation technique is not suitable for mass simulation of hundreds of vehicle types. It is more suited to the detailed analysis of a limited number of vehicles and engines, and there are more suitable methods (employing emissions factors) for estimating emissions for large fleets and large geographical areas, particularly if precise journey details are not known.

Adjusted instantaneous models

Previous parts of this Section have dealt with ‘unadjusted’ models in which the dynamic distortion of the emissions signal has not been taken into account during the model development. Because of the time required to transport the exhaust gas to the analysers, and the actual response time of the analysers themselves, the emission signals measured in a test are delayed relative to their time of formation (and hence relative to the driving cycle). Although this effect is well known, methods for correcting this mis-alignment do not appear to be widely reported (Weilenmann et al., 2001; Atjay and Weilenmann, 2004).

Work on the instantaneous modelling of emissions from modern light-duty vehicles has been conducted in ARTEMIS by the Swiss research institute EMPA. Using advanced measurement and modelling techniques, which are reliant upon knowledge of a number of test parameters, high-frequency measurements, and the solving of a series of differential equations, it is possible to estimate emissions from individual vehicles over short time scales. Weilenmann et al. (2001) have developed a mathematical model of the raw exhaust measurement system which can then be ‘inverted’ or solved in order to

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EURO 2




EURO 1


Pre EURO 1cycle 1 cycle 2 cycle 3 cycle 4 cycle 5 ..... ....


Pre EURO 1

x 3 loadings(empty, 50%, full)

x 6 road gradients(-6%, -4%.....+6%)

Emission factors [g/km] for:FC, NOx, PM, HC, CO

Engine load,FC, emissions

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Nm

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CO2[g/h]

Emission Map

Driving resistances &transmission losses

Gearshift

model

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Gearshift

model

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CO2[g/h]

Emission Map



Gearshift

model

Transient CorrectionTransient Correction

Cold start toolCold start tool

Engine load, FC,Emissions


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reconstruct the original emission signal in the exhaust pipe from the one measured at the analyser. The approach was further developed to compensate for the dynamics in a dilute sampling system (CVS) and initial steps were taken towards developing a new instantaneous model (Atjay et al., 2005). However, no commercially available software has been produced by EMPA so far.

The passenger car part of PHEM was developed as a flexible instantaneous emission model for predicting fuel consumption and emissions for any single type of car, as well as for average car fleets, based on measurements from the ARTEMIS project. The model requires the user to input a driving pattern, and can also take into account the vehicle load, the road gradients and the gear-shift behaviour. The only description in English of the car part of the model appears to be that given by Zallinger et al. (2005). The PC part of PHEM forms part of the main software package, as described earlier.

B.1.7 Other factors affecting hot exhaust emissions

Factors such as the penetration of improved fuels, technologies to increase vehicle efficiency and reduce emissions, and the deterioration of vehicle exhaust emissions with age will have effects on the baseline emission functions for hot exhaust emissions derived from measurements. These are normally taken into account by applying adjustment factors to the baseline functions, based on assumptions concerning levels of uptake and effectiveness.

For example, one means of controlling emissions is through the specification of fuel quality and consistency. The gradual introduction of advanced exhaust after-treatment technology has led to a requirement for fuels with low sulphur content. Directive 2003/17/EC requires that ‘sulphur-free’ petrol and diesel (defined as having less than 10 ppm of sulphur) be made available from 2005, with the full transition to sulphur-free fuels by 1 January 2009. In addition, the promotion and use of biofuels and other renewable fuels for use in transportation was moved forward in Directive 2003/30/EC. The directive proposes a target of 2% market penetration by the end of 2005, rising to 5.75% by the end of 2010.

CO2 emissions from the newest vehicles will be influenced by the general improvements in technology designed to increase fuel economy and, for cars in particular, by a voluntary agreement between the European Automobile Manufacturers Association and the EU to reduce emissions, as well as changes in fuel composition (e.g. the wider adoption of alternative fuels).

B.2 Cold-start emissions

During the period following an engine start, emissions and fuel consumption are elevated as a result of incomplete combustion of the fuel in the engine, the low conversion rate of pollutants in the catalyst, increased viscous friction due to the low lubricant temperature in the engine and transmission, and increased rolling resistance in the tyres. These ‘cold-start’ emissions can constitute a significant proportion of total road transport emissions, particularly in urban areas. Cold start emissions and fuel consumption will be higher for lower engine starting temperatures which will, in turn, depend upon factors such as the length of time the vehicle has been parked and the prevailing ambient temperature. Cold start emissions are usually included in regional and national emission inventories, but are often excluded from local air pollution studies. One of the main reasons for the exclusion is the difficulty associated with the allocation of cold start emissions in time and space. A number of cold start emission models are summarised below.

B.2.1 COPERT


TRL 66 PPR473

In COPERT, the ratio of cold to hot emissions (ecold/ehot) is applied to the regional or national fraction of kilometres driven with a cold engine (ß), and is multiplied by the aggregate hot emission estimate for the area:

The ß parameter depends upon ambient temperature (ta) and pattern of vehicle use, in particular the trip length (l trip), and is given by:

atriptrip tllβ ××−−×−= )0.000385(0.009740.025450.6474

Average national values are used for ta and ltrip. The introduction of more stringent emission standards for catalyst-equipped gasoline vehicles has resulted in earlier catalyst light-off. In the calculation, this is represented as a reduction in the ßparameter. The value of the ecold/ehot ratio also depends on vehicle speed, ambient temperature, and the pollutant considered. These dependencies are partially accounted for in COPERT.

B.2.2 MEET

The MEET model incorporates a slightly different cold start routine (European Commission, 1999). A reference cold-start excess emission value is defined for each pollutant and vehicle type (passenger cars only) as the value corresponding to a start temperature of 20oC and an average trip speed of 20 km h-1. The reference value can then be corrected for the actual start temperature and average speed, and also for the distance travelled (as some trips are shorter than the distance needed to fully warm the engine). The excess cold start emission is expressed as follows:

Where:

V = mean speed in km/h during the cold period.

T = temperature in oC (ambient temperature for cold start, engine start temperature for starts at an intermediate temperature).

d = distance travelled.

ω = reference excess emission at 20oC and 20 km h-1.

As there are very few data relating to intermediate starting temperatures, it is assumed that the effect of starting when the engine temperature is higher than the ambient temperature is equivalent to a cold start at the temperature of the engine. Corrections to the excess emission for trips shorter than the cold distance are expressed in the MEET model as a non-linear function of the ratio of the trip length to the cold distance:

Where: δ = the ratio of the trip distance to the cold distance a = a constant

The method for estimating cold start emissions from HGVs is based on the results of tests on ten heavy-duty engines. Operational data for HGVs, giving the number of cold starts per day, are not included. The same cold start emission factors are used for buses and coaches, but again there are no precise operational data relating to cold starting or occupancy.

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TRL 67 PPR473

B.2.3 EXEMPT

Blaikley et al. (2001) developed a model to predict cold start emissions for different parking scenarios. The model used emission measurements from 15 vehicles at various ambient temperatures and vehicle starting temperatures. During these tests, regulated emissions, benzene, 1,3 butadiene, and size-differentiated particulate number flux were measured during warm-up.

The model is run in 3 stages:

(i) The initial driving stage. The user defines ambient and engine start temperatures and the distance driven.

(ii) The parking stage. The user defines the ambient temperature and parking time start engine temperature can be specified or calculated from stage (i).

(iii) A further driving stage. The user defines ambient temperature and driving distance start engine temperature can be specified or calculated from stage (ii).

The model also allows the user to define the percentage of the vehicle parc made up of each vehicle category for which experimental data has been collected. The user specifies the total number of vehicles and the conditions for which the model is to be run. The total excess emission of each pollutant is then calculated using this data. The results from this model may be used in conjunction with those from a hot exhaust emission model. The main limitation of this model is the lack of suitable input temperatures and parking duration periods applicable to the UK fleet.

B.2.4 HBEFA

HBEFA uses a traffic situation approach to emission modelling, and the user cannot specify detailed driving conditions. In HBEFA an additional emission is introduced for each start event to allow for the cold-start effect. The user can define the ambient temperature, journey length, soak time, and driving pattern (which determines the proportion of vehicles operating in cold-start mode).

B.2.5 COLDSTART

In Sweden VTI has developed a detailed model called COLDSTART which describes cold-start emissions as a function of ambient temperature, wind velocity, vehicle technology level (including the use of engine heating), parking location, and parking duration (Hammarström and Edwards, 1997). The model includes engine warm-up and cool-down profiles. However, little information on the model is available in English.

B.2.6 ARTEMIS

The ARTEMIS project included the most comprehensive study to date of cold start emissions from European vehicles (André and Joumard, 2005). Three separate models were produced, and these are summarised below.

Model 1

The first model gives an excess emission per start in grammes for a given car type and pollutant, as a function of the ambient temperature T, the mean speed during the cold period V, the travelled distance d, and the parking time t.Ecold(p,T,V, δ,t) = ω20°C, 20 km/h (p) · f(p,T,V) · h(T,δ(p,T,V)) · g(p,t)


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Where:

Ecold = excess emission for a trip (g) p = atmospheric pollutant T = ambient temperature (°C) V = mean speed during the cold period (km/h) δ(p,T,V,d) = dimensionless distance = d/dc (p,T,V)d = travelled distance (km) dc(p,T,V) = cold distance (km) for the pollutant p ω20°C,20km/h(p) = reference excess emission (at 20°C and 20 km/h) for a trip

distance longer than the cold distance, i.e. in any case longer than 15 km (g), for the pollutant p

f(p,T,V) = plane function of the speed V and the temperature T, for the pollutant p

h(p,δ) =

a(p) = constant coefficient for the pollutant p. It corresponds to the shape of the dimensionless excess emission.

g(p,t) = % of excess emission at 12 h of parking as a function of the parking time t for the pollutant p

t = parking time (h)

The values of ω20°C,20km/h(p) and f(p,T,V) are available for each pollutant, regulated or unregulated. Values for h(p,δ) and g(p,t) are not available for unregulated hydrocarbons (URHC). For these, the functions h and g for total hydrocarbons are used.

Model 2

The second model gives an excess emission of the traffic in grammes, as a function of:

• Traffic flow • Season (winter, intermediate, summer, year) • Average speed • Ambient temperature • Hour of the day

Distributions of the distance travelled according to average speed, ambient temperature and parking time are required. Default values are given, but the user can also define the distributions. The model takes the form:

Where:

Ec(p) is the excess cold start emission of the pollutant p corresponding to traffic tfi,h (g)

p is the pollutant

i is the vehicle type

cm(s,vi) is the % of mileage under cold start or intermediate temperature conditions for seasons, overall speed vi and vehicle type i


TRL 69 PPR473

s is the season (winter, summer, intermediate, year)

vi is the overall average speed for the vehicle type i (km/h)

ωi(p) is the reference excess emission for the vehicle type i and pollutant p (g)

h is the hour

tfi,h is the traffic activity for the studied vehicle type i and hour h (vehicle km)

ph is the relative number of cold starts for hour h (average=1)

ptfi,h is the relative traffic for the studied vehicle type i and hour h (average=1)

j is the speed class with a cold engine

m is the trip length class

n is the class of stops (0 – 1/4, 1/4 – 1/2, 1/2 – 3/4, 3/4 - 1, 1 - 2,... , >12h)

pi,j is the % of the distance travelled at speed j with a cold engine, for the overall average speed, and for the studied vehicle type i (%)

pm,j is the % of the distance started with a cold engine and distance dm, for speed Vj with acold engine (%)

ph,n is the % of the distance travelled after a stop with a duration of tn, for the hour h (%)

dm is the average distance of the trips under cold start conditions of class m (km)

f(p,Vj,T) is a plane function of the speed Vj and the temperature T, for the pollutant p

Vj is the average speed with a cold engine corresponding to class j (km/h)

T is the ambient temperature (°C)

h(p,δ) is equal to (1-ea(p,T).δ)/(1-ea(p,T))

a(p) is the coefficient for pollutant p

δ(p,T,Vj,d) is the dimensionless distance = dm/(dc(P,Vj,T)

dc(p,Vj,T) is the cold distance for the pollutant p (km)

g(p,tn) is the % of excess emission at 12h of parking as a function of the parking time tn for thepollutant p

tn is the parking time (h)

Amongst all these parameters, different types of parameter can be distinguished:

• Some are purely internal and should not be modified by the user: ωi(p), f(p,Vj,T),dc(p,Vj,T) and g(p,tn)

• Some parameters are input parameters: i, s, vi, h, tfih, ptfi,h and T.

• Some parameters are internal parameters, but could be modified by the advanced user: cm(s,vi), ph, pi,j, pm,j, ph,n, dm and Vj, ωi(p) and f(p,Vj,T) are given for each pollutant p, regulated or unregulated.

Values for h(p,δ) and g(p,tn) are not available for the unregulated hydrocarbons. Again, for these components the functions h and g for the total hydrocarbons are used.

The inclusion of average speed in model 2 is problematic, because of the possible difference between the average speed during the cold period and the average speed during the whole trip. A trip with an average speed vi is subdivided into a cold phase and a hot phase. The cold phase can have an average speed Vj different from the global speed vi. To calculate the global emission a hot emission, calculated using vi, is added, and the cold excess emission is calculated using Vj:

Etotal (trip) = Ecold (Vj ) + Ehot (vi)

If the distance travelled during the cold phase dc corresponds to an average speed Vj

which is different to the speed of the whole trip vi, the travelled distance under hot


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conditions cannot have an average speed vi, and the global emission should be calculated using the formula:

Etotal (dc + dhot ) = Ecold (Vj ,dc ) + Ehot (Vj ,dc ) + Ehot (Vhot ,dhot )

where Vhot is the average speed of the hot distance dhot.

Model 3

Both the previous two models are not easy to use. The first model needs to be complemented by a model giving the numbers and characteristics of the starts. The second model is the most comprehensive and accurate model, but is especially complex to use, and much of the required information is difficult to obtain. It is possible that the use of this model could lead to misleading results. Therefore, a simplified approach was developed in ARTEMIS, whereby the second model, with all its default values, was executed and the outputs were transformed to give excess cold-start emission factors in mass per unit distance, but with only few open input data. The input variables are season, ambient temperature, average speed and hour of the day. Table of the results are given by André and Joumard (2005). The results for unregulated pollutants are available on demand. However, when applying this model, if the actual traffic distribution is very different from the default distribution, the overall emission calculated during the day can be wrong. In such cases, it is recommend that this model is not used on a hourly basis, but that either the second model is used, or model three is used for the whole day - the summation over the day of the hourly cold excess emissions will be more accurate, but its distribution between the hours will not be accurate.

B.3 Evaporative emissions

Evaporative losses of volatile organic compounds from petrol vehicle fuel systems (tanks, injection systems and fuel lines) occur as a result the diurnal variation in ambient temperature and the temperature changes of the vehicle fuel system which occur during normal driving. Evaporative emissions consist mainly of light hydrocarbons (C4 to C6)(CONCAWE, 1987). Evaporative emissions from diesel-fueled vehicles are considered to be negligible due to the extremely low volatility of diesel fuel.

There are several different mechanisms by which petrol evaporates from vehicles:

(i) Diurnal losses. These occur when a vehicle is stationary and the engine is turned off. Diurnal losses are due to the thermal expansion and emission of vapour, mainly from the fuel tank, as a result of changes in ambient temperature during the day.

(ii) Hot soak losses. These occur when a warmed-up vehicle is stationary and the engine is turned off. In the absence of windblast, more engine heat is dissipated into the fuel system. The increasing temperature causes evaporative emissions.

(iii) Resting Losses. These are identified as a separate evaporative source in some of the more recent studies, and result from diffusion, permeation, seepage and minor liquid leaks. If resting losses are not considered a separate category, they are usually included in the hot soak and diurnal categories, although they can also be considered as background emissions, and independent of diurnal losses. Resting losses do not need an increase in fuel temperature to occur.

(iv) Running losses. These are defined as emissions which occur whilst a vehicle is being driven. The heat emitted from the engine and the changing windblast result in variable temperatures in the fuel system.

(v) Refueling losses. These occur while the tank is being filled and the saturated vapours are displaced and vented into the atmosphere. They are usually attributed


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to the fuel handling chain and not to the vehicle emissions. Vapour recovery systems are implemented to control refueling losses.

Evaporative emissions from motor vehicles are dependent upon four major factors:

• Vehicle and fuel system design. • Ambient temperature and its daily variation. • Gasoline volatility - usually expressed by the empirical fuel parameter known as Reid

vapour pressure (RVP). • Driving conditions (trip length, parking time, etc.).

The effects of these factors on evaporative emissions have been the subject of numerous studies. The earliest studies were carried out in the United States in the 1960s (Wade, 1967), but the first European studies were only conducted twenty years later (CONCAWE, 1987, 1988 and 1990). Within the last decade, few measurements of evaporative emission factors have been made in Europe and consequently evaporative emission models have not been updated. However, a new model for evaporative emissions was produced in the ARTEMIS project, based upon a combination of existing models and recent measurements on modern vehicles (Hausberger et al., 2005). This model ought to be investigated for application in West London.

B.4 Non-exhaust PM emissions

There are a number of non-exhaust processes, involving mechanical abrasion and corrosion, which can result in particulate matter being released directly to the atmosphere. The most important direct emission sources are tyre wear, brake wear, and road surface wear. In addition to direct non-exhaust emissions, material previously deposited on the road surface can be suspended or resuspended in the atmosphere as a result of tyre shear, vehicle-generated turbulence, and the action of the wind. Indeed, of the non-exhaust processes resuspension is likely to be the largest contributor to roadside PM10

40 concentrations. A large number of factors affect the emissions from each source, but these factors have not been fully investigated. Furthermore, the contribution of each source to airborne PM varies considerably, both temporally and spatially (Boulter, 2005).

Non-exhaust particulate matter is important for a number of reasons, including the following:

(i) There are no EU regulations specifically designed to control non-exhaust particle emissions, though some countries have banned the use of studded tyres.

(ii) As exhaust emission control technology improves and traffic levels increase, the proportion of total PM emissions originating from the uncontrolled non-exhaust sources will increase.

(iii) The data relating to the emission rates, physical properties, chemical characteristics, and health impacts of non-exhaust particles are highly uncertain.

There is a general lack of consistency in the definitions, terminology and metrics used in the study and reporting of non-exhaust particulate matter. In particular, where emission factors for resuspension are reported, it is not always clear whether they include primary emissions due to abrasion, and a number of modelling methodologies consider abrasion sources but not resuspension. This often renders the incorporation of data into models, and comparisons of model predictions with earlier studies, rather difficult.

Models for non-exhaust PM emissions are generally rather crude. The use of average brake wear emission factors (in g/km) in models does not seem particularly logical, as

40 PM10 represents the mass concentration of particles passing through a size-selective inlet designed to exclude particles greater than 10 μm aerodynamic diameter.


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the differences in the extent of braking for different traffic situations cannot be taken into account. Such emission factors are likely to over-estimate brake emissions in areas of low brake usage, and under-estimate emissions in areas of high brake use. More detailed methodologies for estimating brake wear emissions are required.

Emission factors for resuspension in Europe are variable, and most of the available information is derived from Nordic countries where the use of studded tyres presents a significant problem. There also appear to be some fundamental difference between non-exhaust processes in the US, and those in Europe. Total PM10 emission factors in the US generally appear to be substantially higher than those in Europe. This may be related to the particularly dry and dusty conditions at the locations of US studies. US prediction models, such as the AP-42 are therefore unlikely to be appropriate to the UK.

Boulter et al. (2006) concluded that the methods for calculating PM emissions due to tyre wear and brake wear, which are presented in the EMEP/EEA Air Pollutant Emission Inventory Guidebook (EEA, 2009), are currently the best available for the UK (and in fact are currently used in the NAEI). The method incorporates corrections for both speed and, in the case of HDVs, vehicle load and number of axles. It is therefore proposed that the EMEP/CORINAIR methods are used for these sources in West London. The Guidebook also gives emission factors for road surface abrasion which can also be used in the West London model, although the values are highly uncertain. There is no widely-accepted UK method for calculating emissions due to resuspension. Boulter et al. (2006) reported emission factors due to resuspension for Marylebone Road in London. Resuspension emissions were found to be around 30% of the magnitude of exhaust emissions, and HDVs were found to be almost entirely responsible for the resuspension of particles. However, given that resuspension is influenced by local conditions, the applicability of the Marylebone Road emission factors to other locations is not known.

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73

A city-wide road traffic emission model for Oxford – scoping report

The whole of Oxford has been declared an Air Quality Management Area (AQMA) and a city-wide Air Quality Action Plan (AQAP) is required. Oxford is also currently developing a city-wide ‘Low-Emission Strategy’ to consider further options for integrating local policies, particularly those relating to transport planning and air quality.

The evidence suggests that reductions in emissions will be required for compliance with air quality objectives. Road traffic is an important source of air pollution in the city, and is therefore being targeted. Moreover, the Council is concerned that air pollution models are underestimating concentrations, and that deficiencies in the modelling of traffic and emissions are partly responsible. The more detailed and accurate estimation of emissions from road traffic (and also ambient concentrations) is necessary for the formulation of effective policies and measures for reducing air pollution in Oxford.

The City Council has commissioned TRL to define the scope of a city-wide emissions model for road traffic, and to provide some recommendations to aid its development. Options for the development of the city-wide model framework are presented and grouped according to indicative cost.

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