an assessment of arterial network using macro and micro simulation models presentation by sabbir...
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An Assessment of Arterial Network Using Macro and Micro Simulation Models
Presentation
bySabbir Saiyed, P.Eng.
Principal Transportation Planner
Regional Municipality of Peel
Brampton, Ontario
17th Annual International EMME/2 Users’ ConferenceCalgary, Alberta
Overview Introduction Macro and micro-simulation models Background
Region of Peel Region of Peel Travel Demand Forecasting Model Transportation Tomorrow Survey
Traffic simulation packages EMME/2 INTEGRATION Synchro & Sim-Traffic
Experimental design and methodology Experimental results and discussions Conclusions and recommendations
Introduction Transportation systems provide vital service to our
community by moving people and goods Operation of transportation systems is an important
concern for elected officials and engineers Many cities are experiencing tremendous growth in traffic Several municipalities do not have sufficient funds to meet
growing travel demands The emphasis is to improve performance of traffic systems One of the solutions is to improve performance of traffic
systems by integrating planning and operational analysis This presentation describes the process of integrating
Regional travel demand model with micro-simulation models such as INTEGRATION, Synchro and Sim-Traffic
Macro Simulation Models Macro simulation models such as Regional Travel
Demand (RTM) models are used by most municipalities to forecast current and future travel demands
These models are used for transportation and land use planning
Generally, they involve 4-step approach involving trip generation, trip distribution, modal choice and trip assignment
Micro-simulation models
Micro-simulation models– an important tool in transportation planning
Micro Simulation Models e.g. INTEGRATION, Synchro, Sim-Traffic, VISSIM, PARAMICS, etc.
Micro simulation models simulate car following and lane change behavior of drivers on a second by second basis
Displays output in the form of animation that shows individual cars, buses, trucks, etc.
These models work at an incredibly detailed level and requires equally detailed data
Provides data on speeds, delays and emissions
Macro and Micro Simulation Models at other Municipalities
Several municipalities are employing macro and micro simulation models
City of Calgary is currently supplementing Regional Transportation Demand (RTM) model by using micro-simulation models developed using Vissim
City of Edmonton is also employing micro-simulation models to analyze and design LRT expansion project
City of Toronto is using PARAMICS to evaluate and test ITS initiatives
Region of Peel is using micro-simulation models for analyzing arterials and freeways in addition to RTM
Region of Peel is strategically located…
Region of Peel Region of Peel is 2nd largest municipality in Ontario, 5th
largest in Canada and it is growing rapidly Serves over 1 million residents It covers City of Mississauga, City of Brampton and
Town of Caledon Provides services such as health, regional planning,
housing, transportation, water, sewers, and other municipal services
Rapid population growth and commercial development have transformed what was primarily a rural area of farms and villages into a dynamic blend of urban, industrial and residential areas.
Images of Peel
Region of Peel Model - Background
Regional staff developed the Peel Region’s first travel demand model in 1978
Model was being run on mainframe computer using modeling software developed by MTO and United States DOT (UTPS package)
Acquired emme/2 software in 1989 and Regional staff developed the simplified version of model
Model was calibrated/validated using 1986 TTS and Cordon Count data
Since then model is updated on continuous basis
Region of Peel Model
Four staged model consisting of Trip generation Trip distribution Modal split Trip assignment
Model simulates a.m. peak hour trips Model uses land use and transportation data from
Transportation Tomorrow Survey (TTS) and Census It is validated using Cordon Count data and counts
obtained from traffic and transit departments Several scenarios has been developed for existing and
horizon years such as 1996, 2001, 2011, 2021 and 2031
Structure of Region of Peel model
Trip Generation External Trips Airport Trips
Trip Distribution Apply Growth Factors Apply Growth Factors
Modal Split
Auto Occupancy
Trip Assignment
Peel Traffic Zone System
Traffic zone system used by Peel’s Model is based on Greater Toronto Area (GTA) zone system
There are over 500 traffic zones in Peel and GTA Level of details vary over GTA Zone system is fairly detailed within Peel, with
diminishing level of details away from boundary City of Toronto contains large number of zones due
to its size and trips to and from downtown Oakville has been coded in fine detail Mode split model has been aggregated in 27 zone
groups and occupancy model in 47 zone groups
PeelPeel
HaltonHalton
YorkYork
TorontoToronto
DurhamDurham
Transportation Tomorrow Survey Transportation Tomorrow Survey is an important O-D Survey
conducted by Regional Municipality of Peel, the Province of Ontario, 15 other municipalities in Southern Ontario, GO Transit and Toronto Transit Commission
The most recent survey was completed in 2001, with the previous ones carried out in 1986, 1991 and 1996
The trip data contains information about the household and trips made by each person in the household including trip origin, trip destination, trip purpose, start time and mode of travel
This data is geo-coded and data is available for input into Emme/2 and other models
The O-D matrix developed for the analysis in this paper is based on the data collected from TTS survey and is used as input both for Emme/2 and INTEGRATION software
Traffic simulation packages
Traffic simulation packages used in this study are: Emme/2 Synchro and Sim-Traffic INTEGRATION
The transportation network was created using Emme/2 transportation planning software
Synchro and Sim-Traffic were used to model pre-timed and actuated signal control
INTEGRATION was used to simulate adaptive signal controls
Emme/2 Software
Emme/2 is an interactive multi-modal transportation planning software used worldwide for over 20 years
It offers a complete and comprehensive set of tools for demand modeling, multi-modal network modeling and analysis for implementing evaluation procedures for transportation planning
Its data bank is structured to permit simultaneous descriptions, analysis and comparison of several transportation planning scenarios
In this study, emme/2 is used to develop and code transportation network and to generate O-D matrix for input in INTEGRATION model
Synchro
Synchro is a complete software package for modeling and optimizing traffic signal timings
It optimises cycle lengths, splits, offsets and phase orders
Synchro also optimises multiple cycle lengths and performs coordination analysis
Synchro can analyse pre-timed and actuated signal control systems
It can optimise the entire network or group of arterials and intersections in a single run
Synchro has colourful, informative time-space diagrams It provides more than 17 reports on several measures of
effectiveness of signalized intersection
Sim-Traffic
Sim-Traffic is companion traffic model that comes with Synchro and it is a microscopic simulation model
It is designed to model networks of signalized and unsignalized intersections
It can be used to check and fine tune traffic signal operations and is useful for analyzing complex situations such as closely spaced intersections and intersections under heavy congestion
It can model pre-timed and actuated signal controls Each vehicle in the traffic network is individually tracked
through the model and comprehensive measures of effectiveness are recorded during simulation
INTEGRATION Model
Developed in late 1980s by late Dr. M. Van Aerde with extensive support of MTO
INTEGRATION model is an attempt to provide a single model that could consider both freeways and arterials as well as traffic assignment and simulation
This ability is intended to bridge a gap between the planning models as well as traffic operational models/tools
INTEGRATION model can also model Intelligent Transportation Systems such as ATMS and ATIS.
It can also be used for evaluating TDM (HOV) policies, goods movement (truck sub network), toll roads, intersection improvements, etc.
INTEGRATION Model It models the interactions of individual vehicles with
freeways, arterials, traffic signals and ITS, while preserving macroscopic properties of each link in the network
The model uses Dynamic Traffic Assignment (DTA) in addition to Static Traffic Assignment
DTA allows vehicles to reroute according to current traffic conditions of the network
INTEGRATION does not require the user to collect input data at the individual vehicle level
It uses O-D traffic demands and therefore EMME/2 data can be used effectively
The model uses internal logic to determine microscopic measures such as free speeds and densities
Experimental Design Transportation network was created in emme/2 software
based on real network of Region of Peel with minor modifications to number of lanes and capacities
The zone centroids represents the traffic zones of Peel Region
A traversal matrix was developed for the study area based on actual O-D survey data
There are 26 nodes, 58 links and 77 O-D demand loadings The saturation flow rate was set to regional standards,
which is 1900 vehicles/hour, consistent with typical high grade urban network
The inter-green time was set to 4 seconds of Amber and 2 seconds for all Red
Experimental Methodology
The arterial network and a traversal matrix was developed using Emme/2
This network was batched out from Emme/2 and was entered in INTEGRATION software
Additionally, the data could be imported into Excel spreadsheet for further changes
All the essential files were created for the INTEGRATION model and it was run to simulate traffic demands
The turning movement generated using INTGRATION were entered into Synchro to simulate pre-timed and actuated traffic demands
Arterial Network
Types of Signal Control Traffic engineers can maximise performance of traffic signal
by varying cycle time, green splits, offsets and phase types as well as sequencing
There are three types of signal control Pre-timed Actuated Adaptive
In pre-timed signal controls , there are fixed time plans and time of day plans
In actuated signal controls, controller operates on traffic demands based on actuation of vehicles and pedestrians
In adaptive signal controls, no preset plans are developed; new signal timing plans are computed dynamically based on prevailing traffic demands
Network Totals before Optimisation
Pre-timed Actuated Adaptive
Total Signal Delay (hr) 90 42 42
Stops/Veh 0.79 0.3 0.45
Total Stops 13049 5045 7698
Average Speed (km/hr) 47 52 50
Total Travel Time (hr) 450 403 425
Distance Travelled (km) 21050 21050 21050
Fuel Consumed (litre) 2509 2123 2019
CO Emissions (kg) 46.66 39.48 66.55
NOx Emissions (kg) 9.01 7.62 3.72
Pre-timed Actuated Adaptive
Total Signal Delay (hr) 32 27 24
Stops/Veh 0.46 0.45 0.26
Total Stops 7678 7545 4612
Average Speed (km/hr) 54 54 54
Total Travel Time (hr) 392 387 416
Distance Travelled (km) 21050 21050 21050
Fuel Consumed (litre) 2171 2156 1900
CO Emissions (kg) 40.38 40.11 56.26
NOx Emissions (kg) 7.79 7.74 3.22
Network Totals – Cycle/Offsets Optimisation
Total Signal Delays
Total Signal Delay
0
20
40
60
80
100
Pre-timed Actuated Adaptive
Signal Control Types
To
tal
Sig
na
l D
ela
y (
hr)
Before Optimisation
After Cycle Optimisation
After Offset Optimisation
After Cycle/OffsetOptimisation
Conclusions and Recommendations
Emme/2 could be effectively utilized to develop a regional travel demand model
Transportation network could be easily developed using Emme/2 for input into micro-simulation model
Emme/2 could be used to develop sub-area model and also for developing traversal matrix
Emme/2 could be easily integrated with micro-simulation models such as INTEGRATION, Synchro and Sim-Traffic to provide additional measures of effectiveness for arterial network for transportation planning and operational analysis
Conclusions and Recommendations
INTEGRATION offers Dynamic Traffic Assignment method in addition to traditional methods of assignment
Sim-Traffic and the INTEGRATION models produce an on-line simulation display that can be efficiently used to visualize traffic flow and to analyze the measures of effectiveness of the network
Sim-Traffic could be used to simulate and animate to determine operational level traffic problems
Synchro could be effectively used to determine macro level LOS and delays
Conclusions and Recommendations
The experiment also demonstrates that Synchro, Sim-Traffic and INTEGRATION could be used to analyze pre-timed, actuated and adaptive traffic signal controls
It is shown that optimization improves the performance of the arterial network
It is recommended that further work should be carried out to examine medium and large network using above methodology
The results of the experiment would provide additional information and a better understanding of several measures of effectiveness for effective transportation planning and operation analysis
Thank you
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