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Acknowledgements

Acknowledgements

My work at the Royal Institute of Technology, Stockholm, Sweden, has been sponsoredby three main projects: SINDI (Study of Safety Indicators), EMV (Writing of the Swedish

Effect-Outcome Catalogue), and VTLKomb (Evaluation of Traffic Management andControl Systems on Networks and Link Roads). All three of these projects have beenjointly sponsored by VINNOVA (formerly KFB) and the Swedish Road Authority(Vgverket).

Other work has also been carried out on projects at SWECO VBB. This includes thesafety evaluation study of signalled intersections on Hornsgatan, using the SwedishTraffic Conflict Technique financed by the Stockholm Office for Road and Real-EstateManagement (Gatu- och fastighetskontoret). Work in relation to the LHOVRA incidentreduction function has been carried out in close co-operation with Azhar Al-Mudhaffar atthe Royal Institute of Technology.

Thanks to all at the Centre for Traffic Simulation Research (CTR) and the Division forTransport and Logistics (ToL) at the Department of Infrastructure, Royal Institute ofTechnology, Stockholm, Sweden. Special thanks to professors Karl-Lennart Bng (ToL)and Ingmar Andrasson (CTR) for their support and assistance throughout thepreparation of this thesis.

Stockholm, 3rd December 2004

Jeffery Archer

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Table of Contents

Table of Contents

PART I: INTRODUCTIONChapters 1-6

1. General Introduction ............................................................................2

1.1 Background ..........................................................................................................21.2 Aims of this Thesis ...............................................................................................31.3 Limitations ............................................................................................................ 4

2. Traffic Safety Definitions and Concepts ........................5

2.1 The Science of Traffic Safety ............................................................................... 52.2 Traffic Safety as a Scientific Construct.................................................................62.3 Basic Dimensions of Traffic Safety.......................................................................82.4 A Systems Theory Approach to Traffic Safety......................................................92.5 Safety Continuum and Safety Indicators ............................................................102.6 Crashes and Accidents ......................................................................................122.7 Traffic Safety, HMI and Systems Safety.............................................................12

3. Safety Modelling Approaches and Theories............13

3.1 Categories of Safety Models and Theories ........................................................133.1.1 Descriptive Models ............................................................................................. 13

3.1.2 Predictive/Analytical Models............................................................................... 14

3.1.3 Risk Models ........................................................................................................ 15

3.1.4 Accident Consequence Models .......................................................................... 173.2 Levels of Abstraction in Safety Modelling...........................................................183.3 Traffic System Simulation and Modelling ...........................................................19

3.3.2 Classifying Simulation Models............................................................................ 20

4. Traffic Safety and Urban Areas ...................................................23

4.1 The Scope of the Problem..................................................................................234.2 Traffic Safety at the International Level..............................................................23

4.2.1 International Data Collection............................................................................... 23

4.2.2 International Accident Statistics.......................................................................... 24

4.3 Traffic Safety at the National Level: Sweden......................................................264.3.1 Safety Data Collection and Analysis in Sweden................................................. 26

4.3.2 Accident Statistics in Sweden............................................................................. 27

4.3.1. Accident Types and Road-User Risk Levels ..................................................... 28

4.3.2 Alcohol and Drugs .............................................................................................. 29

4.3.3 Accidents in Urban Areas vs. Rural Areas and Motorways................................ 29

4.3.4 Accidents at Intersections................................................................................... 31

4.3.5. Accident Data Quality and Underreporting........................................................ 32

4.4 Summary: Traffic Safety in Urban Areas............................................................34

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5. Traffic Safety Policy ...............................................................................35

5.1 European Traffic Safety Policy ...........................................................................355.2 Safety Policy in Other Countries ........................................................................365.3 Socio-Economics and Traffic Safety...................................................................37

5.4 Traffic Safety Policy in Sweden..........................................................................385.4.1 The Role and Organization of the SRA .............................................................. 39

5.4.2 SRA Road Safety Policy..................................................................................... 40

5.4.3 High-Level Traffic Planning and Evaluation........................................................ 40

5.4.4 Towards Safer Traffic ......................................................................................... 41

5.4.5 The STRADA Database...................................................................................... 42

6. Traffic Accident Causation and SafetyCountermeasures in the Urban Environment................43

6.1 Accident Analysis ...............................................................................................43

6.2 Large-Scale (Longitudinal) Accident Studies .....................................................436.2.1 The UK Study...................................................................................................... 44

6.2.2 The French Study ............................................................................................... 44

6.2.3 The American Study ........................................................................................... 45

6.2.4 Other Studies...................................................................................................... 45

6.3 Accidents and Traffic System Complexity ..........................................................466.4 Human Error and Accident Causation................................................................46

6.4.1 Human Behaviour and Error Taxonomy Models ................................................ 49

6.5 Safety Countermeasures....................................................................................516.5.1 Safety Measures aimed at Road-users .............................................................. 52

6.5.2 Safety Measures aimed at Vehicles (and their occupants) ................................ 53

6.5.3 Safety Measures aimed at the Roadway Environment ...................................... 536.6 Intelligent Transport Systems (ITS) in Urban Areas ..........................................556.7 Conclusions: Traffic Accident Causation and Safety Countermeasures ...........56

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PART II: SAFETY MEASUREMENT TECHNIQUESAND PREDICTIVE MODELLINGChapters 7-10

7. Traffic Safety and Predictive Modelling ..............................59 7.1 Predicting Safety Impact: SRA Safety-Models ...................................................597.2 Predictive Safety Impact Modelling: Other Models.............................................607.3 Accident Prediction Based on Speed .................................................................617.4 Accident Prediction Based on near Accidents .................................................62

8. Short-term Traffic Safety Assessment and SafetyIndicator Measurement Techniques .......................................65

8.1 Proximal Traffic Safety Indicators.......................................................................65

8.2 The Traffic Conflict Technique ...........................................................................668.3 Time-to-Collision (TTC) ......................................................................................688.3.1 Extended Time-to-Collision (TET, TIT)............................................................... 70

8.3.2 Time-to-Zebra (TTZ) ........................................................................................... 70

8.4 Post-Encroachment Time (PET) ........................................................................718.4.1 Derivatives of Post-Encroachment Time ............................................................ 72

8.5 Other Safety Indicators.......................................................................................728.6 Safety Influencing Factors..................................................................................74

8.6.1 Speed and Speed Variance................................................................................ 74

8.6.2 Gap-Acceptance................................................................................................. 76

8.6.3 Red-Light Violations............................................................................................ 78

8.6.4 Traffic Flow, Traffic Composition, Turning Percentages and Traffic Regulation 78

9. Case Study I: Application of the Traffic ConflictTechnique for the Assessment of Safety atSignalized Intersections on a Major City Street ...79

9.1 Background ........................................................................................................809.1.1 The Study Area................................................................................................... 80

9.1.2 Vehicle Traffic ..................................................................................................... 81

9.1.3 Pedestrian and Cycle Traffic............................................................................... 82

9.1.4 Traffic Safety: Review of Historical Accident Data ............................................. 839.1.5 Safety Indicators and the Traffic Conflict Technique.......................................... 87

9.1.6 Accident Prediction and Conflict Observation Data............................................ 87

9.1.7 Scope and Objectives......................................................................................... 89

9.2 Method ...............................................................................................................899.2.1 Conflict Observation ........................................................................................... 89

9.2.2 Description of Intersections ................................................................................ 91

9.3 Results ............................................................................................................... 969.3.1 Hornsgatan Ringvgen.................................................................................... 97

9.3.2 Hornsgatan Torkel Knutssonsgatan .............................................................. 100

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9.3.3 Hornsgatan Mariatorget ................................................................................. 103

9.3.4 Hornsgatan Sderledstunnel ......................................................................... 106

9.3.5 Intersection Comparison................................................................................... 110

9.3.6 Predicting Accidents on the Basis of Conflict Data........................................... 111

9.3.7 Inter-Observer Reliability .................................................................................. 113

9.4 Discussion and Conclusions ............................................................................ 114

10. Case Study II: Comparison of Proximal SafetyIndicators at Three Urban T-Junctions ........................117

10.1 Introduction.....................................................................................................11810.1.1 Safety at Unsignalized Junctions.................................................................... 119

10.1.2 Safety and Gap-Acceptance........................................................................... 120

10.1.4 Urban T-junction Safety Statistics .................................................................. 121

10.1.5 Urban T-junction Safety Countermeasures.................................................... 122

10.1.6 Relationships among Safety Indicators and other Traffic Data ...................... 123

10.1.5 Scope and Objectives..................................................................................... 12410.2 Data Collection and Analysis..........................................................................124

10.2.1 Choice of T-Junctions..................................................................................... 124

10.2.2 T-Junction Terminology .................................................................................. 125

10.2.3 T-Junction Regulation..................................................................................... 126

10.2.4 Traffic Measurement....................................................................................... 126

10.2.4 Conflict Observation According to the Traffic Conflict Technique .................. 126

10.2.5 Photometric Video-Analysis............................................................................ 127

10.2.6 Safety Indicator Thresholds............................................................................ 132

10.2.7 Safety Indicator Severity................................................................................. 132

10.2.8 Classification of Safety Critical Events ........................................................... 13510.2.9 Effect-Relationship Values for Intersection Safety ......................................... 136

10.2.10 Estimating Critical Gaps for Gap-Acceptance Situations ............................. 136

10.3 Results ...........................................................................................................13710.3.1 The Tby (suburban) T-Junction .................................................................... 137

10.3.2 The Sdermalm (urban) T-Junction................................................................ 152

10.3.3 The Vasastan (urban) T-Junction ................................................................... 167

10.4 Comparison of T-junctions from a Safety Perspective ...................................18110.5 Comparison of Safety Indicator Data Collection and Analysis Methods.........18610.6 Safety Indicators: Strengths and Weaknesses...............................................18810.7 Towards an Applied Methodology for Short-term Safety Assessment Based on

Safety Indicators.....................................................................................................19010.8 Final Discussion and Conclusions..................................................................192

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PART III: SIMULATION AS A DYNAMIC APPROACH TOSAFETY ASSESSMENT AND PREDICTIONChapters 11-13

11. Simulation as a Dynamic Modelling Approach toSafety Estimation....................................................................................197

11.1 General Background ......................................................................................19711.2 Simulation Modelling and Traffic Safety.....................................................199

11.2.1 Micro-Simulation Software Programs............................................................. 201

11.2.3 Other Variables with an Identified Safety Influence........................................ 206

11.3 Calibrating and Validating Simulation Models for Safety Estimation..........20911.4 Simulation Modelling and Erroneous Road-User Behaviour......................21111.5 Summary: Simulation and Safety Estimation .................................................212

12. Case Study III: Micro-simulation as a Methodfor Safety Estimation at a Suburban T-junction 213

12.1 General Background and Research Strategy.................................................21412.2 T-junction Test Site and Data Collection and Analysis...................................215

12.2.1 Traffic Flow Data, Turning Percentages and Traffic Composition Data......... 215

12.2.2 Speed Measures............................................................................................. 215

12.2.3 Car-Following and Vehicle Arrival Data.......................................................... 216

12.2.4 Gap-Acceptance Data .................................................................................... 216

12.2.5 Vehicle Acceleration and Deceleration........................................................... 218

12.2.4 Safety Indicators: Traffic Conflicts (TAs), TTCs and PETs ............................ 21812.4 Simulation.......................................................................................................21812.4.1 Choice of Simulation Tool............................................................................... 218

12.4.2 The VISSIM Micro-simulation Tool................................................................. 219

12.4.3 VISSIM and VisVAP ....................................................................................... 222

12.4.4 Modelling Gap-Acceptance Behaviour in the Simulation Environment .......... 223

12.4.5 Simulation Model Limitations.......................................................................... 226

12.4.6 Simulation Model Output Data and Post-processing...................................... 226

12.5 Simulation Experiment ...................................................................................22812.6 Simulation Model Calibration..........................................................................228

12.6.1 Number of Simulation Runs............................................................................ 228

12.6.3 Car-following Behaviour and Time-Gap Distributions .................................... 22912.7 Simulation Model Validation ...........................................................................240

12.7.1 Traffic Flow Rates and Turning Movements................................................... 240

12.7.2 Speed and Speed Variance............................................................................ 240

12.7.3 Time-Gap Distributions on both Priority Road Directions............................... 241

12.7.4 Accepted and Rejected Time-Gap Distributions............................................. 242

12.7.5 Validation Summary........................................................................................ 243

12.8 Main Results Safety Indicators....................................................................24312.8.1 Simulated Conflict (Time-to-Accident) Results............................................... 244

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12.8.2 Simulated Time-to-Collision Results............................................................... 248

12.8.3 Simulated Post-Encroachment Time Results ................................................. 250

12.4 Discussion and Conclusions ..........................................................................253

13. Case Study IV: Micro-simulation as a Method

for Estimating the Safety and PerformanceEffects of an Incident Reduction Function..............257

13.1 Introduction.....................................................................................................25713.1.1 Traffic Safety and the LHOVRA IR-function ................................................... 259

13.1.2 Suggested Improvements to the LHOVRA IR-Function ................................. 262

13.1.3 Testing Alternative Signal Controller Logic..................................................... 263

13.1.4 Safety Measurement....................................................................................... 263

13.1.5 Scope and Objectives..................................................................................... 264

13.2 Method ...........................................................................................................265

13.2.1 Intersection Test Site ...................................................................................... 26513.2.2 Data Collection and Analysis.......................................................................... 266

13.2.3 Simulation Modelling....................................................................................... 273

13.2.4 Modelling Signal Controller Logic and LHOVRA in VisVAP........................... 274

13.2.5 Simulation Model Calibration Criteria ............................................................. 275

13.2.6 Simulation Model Validation Criteria............................................................... 275

13.2.7 Simulation Experiment and Scenarios............................................................ 276

13.2.8 Simulation Output and Post-processing ......................................................... 276

13.3 Results ...........................................................................................................27713.3.1 Calibration Results.......................................................................................... 277

13.3.1.2 Car-Following and Time-Gap Distribution at Dilemma Zone Entry ............. 278

13.3.2 Validation Results ........................................................................................... 28113.3.2.7 Rear-End Conflicts....................................................................................... 284

13.3.3 Main Simulation Experiment Results.............................................................. 285

13.4 Conclusions....................................................................................................293

PART IV: SYNTHESIS AND CONCLUSIONSChapter 14

14. Synthesis: Methods for Traffic SafetyAssessment and Prediction .....................................................297

14.1 Quick Overview of Thesis...............................................................................29814.2 Safety Indicators: Concepts, Data Collection and Analysis Methods.............30014.2 Towards an New Methodology for Short-term Safety Assessment................30514.3 The Future of Micro-Simulation Modelling for Safety Estimation....................30714.4 Final Discussion and Conclusions..................................................................310

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References...........................................................................................................313

APPENDIX: Software Development .............................................324

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Executive Summary

Executive Summary

To be written

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Glossary of Terms - Quick Reference Guide

Glossary of Terms Quick Reference Guide

Accident (Traffic) Event between road-users that results in injury, fatality or property damage

Accident Causation Underlying reasons that pre-empt a traffic accident, most usually involving anunforeseen chain-of-events. Accident causation is often attributed to one of the

three main components of the traffic system: road-user, vehicle or roadway, or acombination of thereof

Accident Outcome Result of an accident in terms of injury severity, fatality and in some cases alsoproperty damage

Accident Rate Number of accidents in accordance with a measure of exposure

Accident Risk Risk for accident involvement (for different road-user classes). Objective riskreflects accident frequency in relation to a measure of exposure or population

Accident Severity Level of injury incurred in a traffic accident: categorized as minor, serious or fatal

Calibration (Simulation) Process used in Traffic Simulation to (statistically) ensure that the functioning of amodel or specific model component corresponds with observed empirical

measurements or predetermined valuesCar-Following Term used to describe the state of a vehicle that has a time/distance gap or

headway less than a predetermined maximum value

Collision Impact event between two or more road-users/vehicles, or a road-user (vehicle)and stationary object

Collision Course Existence of a common projected conflict point in time and space for two or moreroad-users vehicles, usually based on momentary measures of trajectory, speedand distance

Conflict An interactive event that requires evasive action (braking, swerving oraccelerating) by one or more road-users to avoid a collision

Conflict Distance A momentary measurement of spatial distance to a common conflict point for aroad-user (vehicle) in a conflict situation

Conflict Observation Method that is used by trained observers to determine Time-to-Accident values inaccordance with the Traffic Conflict Technique. Based on the subjective estimationof speed and distance for road-users/vehicles that are on a collision course

Conflict Point Common spatial location of projected trajectories given momentary measures ofspeed and distance for two or more road-users/vehicles

Conflict Zone Common area used by road-users/vehicles approaching from different trajectories

Conflict Severity Seriousness of a potential collision or near-accident measured by temporal orspatial proximity

Conflict Speed Momentary measurement of velocity for a road-user (vehicle) in a conflict situation

Crash Term that is sometimes preferred to (traffic) accident due to the fact that it impliesan element of causality rather than an unforeseen random occurrence

Critical Gap Average measure of gap-acceptance in a yielding situation where the probability ofacceptance is estimated to be equal to the probability of rejection, mainly used forcapacity calculation

Dilemma Zone Area on an approach road to a signal regulated intersection where the driver isindecisive regarding whether to stop or go when faced with amber in accordancewith his/her speed level and the distance remaining to the stop-line

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Driver Behaviour Largely misused and over-simplified term used in traffic engineering that is oftenused to describe the actions and/or variability of drivers in different drivingsituations. Should relate to the study of individual behavioural processes thatunderlie actions in relation to the driver

Driver Performance Generally refers to the skill and ability level of drivers in relation to the driving task

Evasive Manoeuvre Action that is taken by a road-user to resolve a conflict situation and involvesbraking, accelerating, and/or swerving

Exposure Measure of spatial or temporal duration in the traffic system in relation to thenumber of dynamic system objects road-users, vehicles (axles), etc.

Fatality Death resulting from a traffic accident (usually within a 30 day period after theaccident occurrence)

Gap-Acceptance Process that describes and measures interaction between prioritized and non-prioritized road-users. Generally involves spatial or temporal measurement of gapsor lags in prioritized streams that are accepted or rejected in relation to a particularyielding manoeuvre

Incident Reduction (IR)Function Function designed to reduce or eliminate the occurrence of accidents. In Swedishvehicle actuated signal control the IR (O)-function uses vehicle detection to

reduce the number of rear-end accidents and conflicts in the dilemma zone on asignalled stop-line approach

Injury Accidents Traffic accidents that result in minor or serious injury to one or more parties. Somestatistical measures and accident risk quotients include accidents that involve bothinjury and fatality.

LHOVRA LHOVRA is an acronym where each letter represents one of a set of specialfunctions that can be applied in vehicle actuated signal control to improve trafficsafety and/or traffic performance

Near-Accidents Conflict events that have close temporal or spatial proximity to an accident

Non-serious Conflict Conflict event in accordance with the Traffic Conflict Technique that is not ofsufficient severity with regard to measures of speed and distance to be classed asserious

Police Reported

Accidents

Accidents that are reported to the police and are recorded in the database ofaccident statistics (in Sweden only accidents involving injury and fatality arerecorded)

Post-Encroachment

Time (PET)

A measure of accident proximity (i.e. a safety indicator) in which the temporaldifference between two road-users over a common spatial point or area is below apredetermined maximum threshold value (typically 1 to 1.5 seconds). PETmeasures do not require the existence of a collision course (as in Time-to-Accident and Time-to-Collision measures) but do infer crossing trajectories

Process Validity Term that is used to describe the relationship between the processes precedingaccidents and those preceding serious conflicts (or other safety indicators)

Predictive Validity Term that is used to describe the relationship between the expected (predicted)number of (police reported) traffic accidents and the expected number of seriousconflicts (or other safety indicators)

Required Braking Rate

(RBR)

Measure of conflict severity based on a momentary measure speed and distanceto a conflict point, that represents the average (linear) braking required to avoid acollision from the point the measure is taken

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Safety Freedom from accident or losses (see Traffic Safety)

Safety Continuum Theoretical concept inferred in relation to the use of proximal safety indicatorswhereby all interactions are placed on the same scale with safe passages at oneextreme and (fatal) accidents at the other

Serious Conflict Conflict event in accordance with the Traffic Conflict Technique that is of sufficient

severity with regard to measures of speed and distance to be classed as serious

Safety Critical Event Term used to describe a situation where there is an identified accident potential orwhere a proximal measure of safety is below the maximum threshold criteria

Simulation Abstract imitation of the operation of a real-world process or system over time. Intraffic simulation the processes of the traffic system are imitated at various levelsof abstraction

Time Extended TTC

(TET)

Safety indicator measure based on Time-to-Collision. Represents a measure theperiod of time during which vehicles in conflict are under the maximum TTC-threshold. Can be summated for a specific site and measurement period

Time Integrated TTC

(TIT)

Safety indicator measure based on Time-to-Collision. Represents a measure of

the integral of the TTC-event while under the maximum TTC-threshold (i.e.difference from threshold multiplied by time-resolution). Can be summated for aspecific site and measurement period

Time-to-Accident (TA) Safety indicator measure determined in accordance with the Traffic ConflictTechnique. Based on the (subjective) estimation of speed and distance by trainedobservers for conflicting road-users in relation to a common conflict point. TheTime-to-Accident measure is recorded at the time when evasive action is taken byone of the conflicting road-users

Time-to-Collision (TTC) Safety indicator measure based on the continual measurement of speed anddistance for conflicting road-users in relation to a common conflict point in order toobtain a minimum Time-to-Collision value that is below a predetermined thresholdvalue usually between 3 and 4 seconds.

Traffic Conflict

Technique (TCT)

Technique/methodology developed by Christer Hydn at the University of Lund inSweden for the measurement of traffic conflicts. Currently, the only recognizedproximal traffic safety indicator that has a validated relationship with accidents

Traffic Safety Term that is related to the negative performance of the traffic system to generatetraffic accidents that involve injury or fatality. At the individual level, traffic safety isrelated to the absence of danger and experience of security

Traffic System Systems theory view used to describe the processes of the traffic system asdynamic and complex interactions between and among elements at various levels.The three main elements are usually identified as: the roadway infrastructure, theroad-user, and the vehicle

Underreporting Term used to describe the fact that many accidents are not reported to the policeand therefore are not represented in accident statistics

Validation/Validity Validity concerns the accuracy with which a measure represents a theoreticalconstruct (assessed often through consensus). In simulation, validation refers tothe process of ensuring that measures generated by the model correspond (withinstatistical tolerances) to measurements from the field

Vulnerable Road-Users

(VRUs)

Term generally used to describe pedestrians and cyclists, but may also includemopeds and sometimes also motorcycles in view of their susceptibility to injury inthe event of an accident

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PART I: INTRODUCTION

PART I:

INTRODUCTION

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Chapter 1: General Thesis Introduction

1. General Introduction

1.1 Background

The main goals of traffic engineering are focused on the planning, design and operation oftraffic facilities and various parts of the traffic system to best meet the safety, performanceand environmental demands of different road-users given the resources available. Sinceresources are usually a finite factor, there is a commonly a need for optimisation andbalance between different and often conflicting traffic system objectives.

Safety, which is the main concern of this thesis, is commonly measured in terms of thenumber of traffic accidents and the consequences of accidents in relation to their severityand/or fatalities. This is a reactive approach based on historical data i.e. a great manyaccidents need to be recorded before any reliable conclusions can be drawn and thenecessary countermeasures implemented. A further drawback associated with thisapproach is that there are recognized availability and quality problems associated with theaccident data and the methods used for data collection.

In order to estimate and predict levels of traffic safety at different types of intersections,there is a distinct need for the development of newer and more detailed methods andmodels that can successively replace those in existence today. For the purposes of safetyassessment both at the present time, and in the future, it is important to be able toimprove the predictive power of existing models and to introduce effective methods for

traffic safety assessment in the short-term. These methods and models should have theability to allow new and existing safety countermeasures to be assessed on the basis ofindicative measurements taken in the field during a short period of time, and allow theprediction of long-term safety impact with a good degree of validity.

The underlying principle for a more effective safety evaluation strategy is to develop amethodology and predictive models based on proximal safety indicators that represent thetemporal and spatial proximity characteristics of unsafe interactions and near-accidents. Aprerequisite for all safety indicators is that they demonstrate a statistically provenrelationship to accidents and accident outcomes in order to be acceptable as a validmeasure of traffic safety. The principle use of proximal safety indicators, implies a more

efficient assessment, where safety critical events are substantially more frequent thanaccidents and accident outcomes and the methodologies used require only short periodsof observation and analysis.

Proximal safety indicators are particularly useful for before-and-after study designs wherethere is an emphasis on the assessment and comparison of results in relation to theintroduction of safety enhancement measures, and are sensitive to site-specific elementsrelated to roadway design and geometry and the dynamic and complex relationshipsamong many different traffic variables in comparison with many of the more generalstatistical safety prediction models.

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Proximal measures of safety can be generated relatively easily from micro-simulationmodels that are an abstract representation of the real-world study area. The use ofdynamic and often detailed modelling is complex and not without many problems relatedto the calibration and validation process, as well as the representation of traffic behaviourat various conceptual levels.

Simulation is an area that is under rapid development and is constantly opening newdoors for traffic planners as an accepted and versatile tool that can be used to analyseand estimate various performance and safety effects in relation to the traffic system.Simulation has the advantage of taking into consideration the intrinsic dynamic andcomplex nature of the traffic system and thereby represents a useful complement toexisting and more traditional methods of analysis.

1.2 Aims of this Thesis

The work presented in this thesis is concerned with the many concepts, theories and

methods related to short-term traffic safety assessment. Key issues concern the validity,usefulness and potential of various traffic safety indicators as predictors of trafficaccidents and related outcome severity levels. Today, traffic safety is generally measuredin terms of the negative performance of the traffic system, whereby accidents involvinginjury, fatality and property damage occur as the result of inappropriate interactionsbetween road-users, their vehicles, and the roadway that together form the fundamentalelements of the prevailing situational context in the traffic system.

Traffic safety is a particularly difficult phenomenon to study, given the fact that accidentsoccur randomly in time and space thereby making short-term measurement, assessmentand comparison of this concept particularly difficult. Mainly for this reason, there is a need

to further the concepts, methods and measurement techniques that can indirectly be usedto measure safety, where these are more frequent and ultimately valid.

The growing need and quality assurance requirements of modern traffic planning andengineering work demand the use of fast, reliable and effective methods to evaluate andpredict the impact of new and alternative safety enhancement measures at specific trafficsites and in relation to different groups of road-users. These methods must however, alsoconsider the demands for other traffic system objectives, such as: capacity andaccessibility, and environmental issues.

The basic hypotheses underlying the work presented in this thesis concern therelationships between and among different proximal safety indicator measures(frequencies and levels of severity) and how these are influenced by, and related to, othertraffic parameters. Furthermore, it is of importance to identify how the associated conceptsand methodologies can be combined with regard to the ulterior goal of establishingreliable and valid short-term traffic safety assessment methods.

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The following two highly pertinent questions that directly reflect the key hypotheses onwhich this research is based are formulated as follows:

How well do established safety indicators identify different road-user interactionproblems (different conflict types) and how can the advantages associated with theconcepts and methodologies of each be used to obtain a more complete picture of

the safety problems that exist at a particular traffic site ?

What is the extent of the relationship between safety indicators and other trafficparameters, and how can this relationship be described in dynamic models toestimate safety (e.g. using micro-simulation) ?

1.3 Limitations

This thesis is concerned, primarily with road traffic safety from a traffic planning andengineering perspective. Traffic safety is considered from a systems perspective wheresafety critical events are the result of inappropriate interaction between different groups of

road-users, their vehicles if belonging to the driver road-user group, and the trafficenvironment.

There is a particular emphasis on the interactive behaviour between and among vehicledrivers and other classes of road-user. Interactions between vehicle drivers andvulnerable road-users are considered in relation to the use of safety indicators, but are notpart of the micro-simulation studies due to the added complexity and various limitationsassociated with pedestrian and cyclist behaviour in this type of modelling.

Traffic safety is investigated in relation to different types of signalized and unsignalizedintersections that exist in the urban city environment or densely populated outer suburbanareas of Greater Stockholm. Safety related to the rural traffic environment and motorwaysis therefore not considered in this work.

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Chapter 2: Traffic Safety Definitions and Concepts

2. Traffic Safety Definitions and Concepts

2.1 The Science of Traffic Safety

In relation to traffic safety studies, it has been suggested that successful research has twobasic requirements: substantive knowledge of the subject under study, and skill in thepertinent techniques of investigation (Goldstein, 1963). These two perquisites are bothnecessary, and evidence of one element does not assure the presence of the other. Thus,for all studies related to traffic safety one must have a good knowledge of the world ofaccidents and scientific research methodology.

The substantive knowledge of traffic safety that Goldstein refers to is impossible to attainwithout a deeper examination and scientific criticism of the foundations of knowledge that

emanate from the logic of scientific inference and its application to experimental data. Thisknowledge includes concepts of measurement, control, variables, manipulation andanalysis, and theories related to sampling, probability, and mathematical statistics. Amongthe many basic disciplines related to traffic safety are: psychology, sociology, physiology,political science, and most importantly different engineering disciplines and specificunderlying knowledge related to areas such as modelling, systems analysis, computerscience and simulation.

Thus, a multidisciplinary approach is required for the study of traffic safety and it must berealised that people are rarely experts in more than one scientific area. Multi-disciplinaryco-ordination and co-operation must therefore be key aspects in all traffic safety related

scientific research in order to resolve problems of a conceptual methodologicalnature. Inhis philosophically inspiring report, Goldstein identified a number of conceptualmethodological problems that are peculiar to traffic safety research and which do notbecome apparent from the study of only one isolated area. These include the following:

How do we classify safety critical events when accidents almost never occur forthe same reasons, but rather a concomitance of many factors ?

How do we deal with the problem of traffic safety studies when accidents are veryrare events in relation to traffic exposure ? This is particularly significant if abottom-up behavioural approach is adopted. The question of relevant variables,how these are measured and how relevance is established is also asked.

Must we have full knowledge of accident causation to deal with the problem ofaccident prevention ? Accidents can be prevented by removing known hazardsand by redesigning the infrastructure, without knowing the exact nature of realaccident causation. Scientifically, this relationship is important, calling foroperational prevention countermeasures and the combination of knowledgerelation to traffic safety and research methodology.

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Forty years later, traffic safety is still recognised as an area of research that requires amulti-disciplinary approach, and the resolve of conceptual and methodological problemssuch as those identified by Goldstein. In further report from the 1960s, Blumenthal (1968)described the Dimensions of the Traffic Safety Problem and expanded on themultidisciplinary ideas of Goldstein to form an approach that he referred to as the

macrostructure of the traffic safety problem. This approach encompassed the ideas ofgeneral systems theory, political influence and administration policy, and recognised theimportance of values at the individual (microscopic) level.

Based on this macrostructure model, Blumenthal described traffic safety as a problemwith technological, behavioural, and sociological and value dimensions where accidentsoccur as an imbalance between driver capabilities and the demands of the road transportsystem. Each level of the proposed macrostructure was supported by propositions thatoutlined the problems particular to each level.

Blumenthals model is interesting in a number of ways. Firstly, it recognises the

responsibility of an administrative infrastructure for system functionality and anunderlying value structure that represents the increasing social need to take activemeasures. Secondly, it identifies a relationship between these factors and socio-technological problems where driver performance cannot meet the demands of the trafficsystem. This simplified all-encompassing model is still useful in highlighting the multi-structural and multi-disciplinary nature of the traffic safety problem. The importance of thistype of comprehensive approach lies in the ability to understand, explain and predictvariations in the traffic safety situation at the national level.

At an early point in modern road transport development, Smeed (1949) suggested thenotion that society as a whole determines the appropriate level of traffic safetydevelopment based on prevailing needs and demands, and that those aspects of trafficsafety that are most prominent at any particular point in time will be largely the result oftemporary and complex relationships determined by the dynamic forces existing in thecurrent political, social, and economic climate. This view is still very true of the trafficsafety situation in most countries of the world.

2.2 Traffic Safety as a Scientific Construct

In order to be able to study traffic safety there must be a suitable operational definitionthat clearly and distinctly identifies what is and is notrepresented or implied by this term.A good dictionary or thesaurus such as Collins or Oxford English, will typically definesafety in terms of: thequality or condition of being safe, or even: freedom from danger orrisk of injury. If the term is placed before another noun, it is usually used to describe: acontrivance designed to prevent injury or damage. While these definitions are sufficient todescribe the general characteristics of safety, they are less useful in suggesting howsafety may be operationalised for scientific study.

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If safety does indeed represent an absence of danger and/or risk of injury, then anyevent that involves an element of danger or injury risk, ought to be included an integralpart of any safety analysis along with suitable methods that measure their criticality. Animportant question then also refers to what is meant or intended with the terms danger,risk and perhaps even injury. The scope and indeed intention of this thesis is not to

enter into a hermeneutical debate regarding the meaning of intention of different termsrelated to safety (although this subject is both interesting and important from a scientifictheory perspective). Rather, it is important to identify what is generally implied and meantby the terms safety, risk, and danger, and to establish the relationships between them,and recognise that different scientific areas may interpret them differently.

As a scientific concept or construct, traffic safety is usually interpreted (implicitly orotherwise) by policy makers, traffic professionals, the media, and the public asrepresenting, the aggregate number of road traffic accidents that result in fatalities andinjuries of varying severity over a given time-period. Given this widely accepted definition,the notion of freedom from danger or risk of injury is only loosely adhered to, and the

focus is moved to reflect the occurrence and existence of injuries and fatalities as ameasure of what is best described as traffic un-safety.

It is evident that different perspectives are associated with different definitions. Themacroscopic (top-down) perspective is, for example, not directly concerned with thequality or condition of being safe,or the experience of being free from danger or injuryrisk, but instead focuses only on the failures of the traffic system (measured by thenumber of accidents and resulting number of fatalities and injuries). Furthermore, thisdefinition of safety is measured reciprocally and conversely against an ideal of absolutesafety, a definition that is opposed by, amongst others, Leveson (1995).

According to Leveson in her book on the subject of Systems Safety, safety should bedefined as: Freedom from accidents or losses. Where there is no such thing as absolutesafety, safety should be defined in terms of acceptable loss. Deciding the level ofacceptable loss is of course not a trivial issue, and involves complex and ethicallydisputed forms of socio-economic evaluation at a political level.

The more widely accepted definition of traffic safety as representative of the number ofaccidents involving fatality and injury is not accepted by all. Behavioural scientists inparticular, prefer a more humanistic (bottom-up) approach that focuses on the qualitativeand quantitative understanding individual behaviour and performance in relation to

different situational contexts (see e.g. Vogel, 2003). Others too, prefer to let traffic safetybe defined within the realms of a given context (see e.g. Evans, 1991).

There are a number of closely related concepts that are important to distinguish fromsafety at this point. The first of these is danger. Danger refers to the state of beingvulnerable to injury or loss, or risk. Thus, there appears to be no tangible difference in thedescription or experience of events that are dangerous or unsafe since both imply riskand/or vulnerability for injury. The main difference relates to how the terms are used andtheir implied meaning in a modern linguistic sense.

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Risk in traffic can be conceived of as the exposure to, and possibility of danger withregard to personal injury. From a traffic systems macroscopic perspective, accident riskrepresents the numbers of accidents and their outcomes in relation to a measure ofexposure or demographic variable. An individuals subjective perception of risk for injury oraccident involvement, is however, unlikely to be equal to the corresponding objective

measures. This disparity is largely due to individuals lack of awareness of objectiveaggregated risk levels and is exaggerated by humanistic mechanisms that cause anadaptation of behaviour in relation to changing levels of risk (see e.g. Ntnen andSummala, 1976; Oppe, 1988; Wilde, 1988).

To the traffic planner or engineer, traffic safety is closely related to the idea of identifyingsafety problems and developing and introducing measures that counter the occurrence ofroad traffic accidents, most particularly those that result in fatalities and serious injuries.

2.3 Basic Dimensions of Traffic Safety

In light of the above discussion, it is useful to present a descriptive model that is said torepresent the three main dimensions of traffic safety. This macroscopic model suggestedby Rumar (1988), identifies and highlights the relationship between three elementarydimensions including: exposure, riskand consequence.

These measurable dimensions werechosen by Rumar because of the fact that changesin any one dimension will influence the overall traffic safety situation (represented by thethree-dimensional area in Figure 2.1). The structure of this model might be taken to implythat the dimension ofriskis independent ofexposure and consequence. However, riskisof course defined as the product of the number of accidents involving injury in accordancewith a measure of exposure.

Figure 2.1 Three dimensions of traffic safety according to Rumar

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This descriptive model indicates the general principles that underlie traffic safety statisticsused to describe the situation at the macroscopic level. Adding other information, such as,for example, accident location and different demographic details, it becomes possible toperform detailed descriptive analyses and calculate specific risk factors that allowpertinent safety problems to be identified, described, and predicted.

2.4 A Systems Theory Approach to Traffic Safety

As a scientific construct, traffic safety refers to the existence of safety within the trafficsystem. It is useful to consider trafficas an open system in accordance with the GeneralSystems Theory perspective introduced by biologist Von Bertalanffy in the 1940s (seee.g. Von Bertalanffy, 1968). This perspective can be applied in order to describe andexplain the inherent properties, hierarchical structure and the complex and dynamicnature of the traffic system. The systems perspective also provides a means for detailedsystematic analysis and the identification of properties or mechanisms such asemergence that exist as a result of the interconnectivity and relationships of system

components at different structural levels (see e.g. Weinberg, 1975; Casti, 1997).According to this theory, the complexity and dynamic nature of the traffic system can beexplained by the interactions and relationships among vehicles, drivers, and roadwaycomponents at the highest level of abstraction (see Figure 2.2).

Figure 2.2 A simplified conceptual model of the key elements in the traffic system andthe interaction between elements

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Traffic safety and other measures of traffic system performance can be regarded as anemergent property of the traffic system generated by inappropriate (and in many casesunsafe) interactions between or within components as the system transcends from onestate to another over time.

Furthermore, road accidents can be meaningfully viewed as failures of the system ratherthan failures of any single component (Little, 1966). By focusing on the interactionsbetween and among road-users and other objects, it is possible to establish an approachto safety research that can be facilitated by the construction of abstract conceptual modelsand theories. As scientific instruments, these models and theories provide a meanstoward the discovery of new knowledge through the generation of new researchhypotheses that need to be investigated.

The systems approach described here also emphasises the problem of establishing asuitable balance between the different and sometimes opposing measures of systemperformance related to accessibility, safety and environmental issues.

2.5 Safety Continuum and Safety Indicators

It is often argued that traffic safety should be represented as a continuum that stretchesfrom standard safe road-user behaviour and performance at one end to traffic accidents(of varying outcome severity levels) at the other. This continuum allows connectivitybetween the bottom-up approach to traffic safety found in the behavioural sciences, andthe macroscopic interpretation of traffic safety as representing accident frequency andoutcome severity. For such a continuum to be of value, it is necessary to establish therelationships between the various intermediate levels, as proposed by Von Klebelsburg(1964) and others before him (see Figure 2.3).

Figure 2.3Traffic safety and the relationship between errors, standard behaviour, trafficconflicts and accidents

The concept of a safety continuum is of great value in traffic safety research. Most

importantly, it implies that traffic safety can be studied and evaluated through theidentification and measurement of safety critical events that have the characteristics ofaccidents (e.g. braking or swerving), but where no accident actually results (seeSvensson, 1998). Research has shown that the numbers and severities of such near-accidents events have an established statistical relationship with accidents, and in somecases can be a better predictor of the expected number of accidents than historicalaccident data (Migletz et.al. 1985; Svensson, 1992).

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This approach to traffic safety study, is one that was recognised and developed byresearchers at General Motors during the late 1950s and early 1960s and has been usedin the development of the Traffic Conflict Technique by Hydn at the University of Lund inSweden (see e.g. Hydn,1987; 1996). Conflicts of varying severity that are observed fromon-site studies can be ordered in a frequency-of-occurrence hierarchy that relates

accidents at the highest level to safe passages at the lowest level.

This safety continuum is conceptualised as a three-sided pyramid, where differentcategories of safety critical events with varying levels of severity can be identified (seeFigure 2.4).

Figure 2.4 The relationship between safety critical events according to the TrafficConflict Technique proposed by Hydn

The use of measures that represent near-accidents, such as traffic conflicts are commonlyreferred to as proximal indicators of safety, or simply safety indicators. Safety indicatorsimply the existence of the safety continuum and represent measures are statisticallycorrelated with (police reported) accidents and accident outcome severity.

Given the definitions mentioned earlier, one might also argue that accidents themselvesare indicators of the quality of safety. There are researchers that prefer this broaderconceptualization (Vogel, 2003). The use and definition of safety indicators including theTraffic Conflict Technique is discussed in more detail later in this thesis.

A further qualifying distinction with regard to traffic safetyis offered by the CEMT (2002).

Here, a distinction is made between direct and indirect traffic safety measures. The CEMTregard the number of people killed and injured in road traffic as direct safety measuresthat have a sufficient degree of validity, reliability and availability to describe the local,national or international safety situation.

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There are also a number of recognized indirect safety measures that are regarded asindicative of the safety situation, but which do not provide a complete picture.

These include the following:

Number of near-accidents

Level of exposure to road traffic

Behavioural measures

Measures related to roadway and vehicle standards

Traffic legislation and measures of enforcement

Existence of systematic traffic measurements

Awareness of safety problems among the general public

2.6 Crashes and Accidents

There is some debate about whether of not the term crash is more appropriate thanaccident. This is based on the notion of an accidentas an unforeseen event that occurswithout apparent cause, and which therefore cannot be prevented (NHTSA,2002; ECMT,2002). While the crash term may represent a more accurate literary definition, it maynevertheless be inappropriate to describe collisions involving vehicles and pedestrians.For the purposes of this thesis, the term accident is preferred simply because it is agenerally accepted norm in most countries, including Sweden. This definition concurs withthe definition offered by Carsten et.al. (1989) in which accidents are described as beingthe result of a complex and dynamic unfortunate chain of events.

2.7 Traffic Safety, HMI and Systems Safety

Developments and conglomerations of expertise among the many disciplines associatedwith traffic safety have led to the evolution and establishment of new multidisciplinaryareas of research that have slightly different goals and aims, but where the researchfindings in one area have implications for the others.

For traffic safety research, this situation is evident with regard to the area referred to asHMI (Human Machine Interface) that combines vehicle engineering and behaviouralscience to adapt the vehicle-interface to match the needs and limitations of the driver (seee.g. Wickens, 1992). This area of research combined with technological advances inmainstream vehicle engineering has vastly improved traffic safety in recent years. Anothermultidisciplinary area of research is systems safety (see e.g. Leveson.1995). This fieldhas had a major role in the development of Intelligent Transport Systems (ITS) that areused in high-technology vehicle-based systems, as well as infrastructure-based systemsfor traffic management and control.

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Chapter 3: Safety Modelling Approaches and Theories

3. Safety Modelling Approaches and Theories

3.1 Categories of Safety Models and Theories

In 1997, a report was published by the OECD that recognised the need for more multi-disciplinary and diversified road traffic safety research in order to overcome the increasingcomplexities of current traffic problems (OECD,1997). According to this report, theprocess of formulating and testing models and theories provides the key to improving andprioritizing research in the traffic safety area. It also facilitates the accumulation ofknowledge and promotes communication and the establishment of multi-disciplinaryresearch. Four main categories of models are identified by the OECD in relation to trafficsafety research. These are distinguished by their main intention or purpose:

Descriptive models

Predictive / Analytical models

Risk models

Accident consequence models

3.1.1 Descriptive Models

The majority of descriptive models are based on two principle sources of information,accident/casualty data and exposure data. Accident and casualty data are derived largelyfrom official accident registration by authorities such as the police, and can be

supplemented by other sources including: insurance companies, hospital data, accidentinvolvement surveys, self-reported data, and in-depth accident reports. Each of thesesources has known drawbacks, most commonly underreporting, which limits datareliability and to a lesser extent also validity.

In order to interpret accident and casualty data, reliable exposure data is needed. Themost common exposure units are: inhabitants, registered vehicles, vehicle mileage, road-user mileage, vehicle hours, road-user hours, number of trips, and traffic situations.Exposure data is most usually not collected specifically for the purposes of safetyanalysis, and therefore introduces further problems related to validity. There are two maintypes of procedures at the national level for collecting general exposure data: traffic

counting systems, and travel-habit surveys.

Accident/casualty data and exposure data can be used to determine different risk ratios.In Sweden as in many other countries, a large number risk quotients are used asindicative traffic safety measures by policy makers, practitioners, and researchers.

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Some of the most commonly used risk ratios are specified below:

General health risk Number of fatalities/injuries per person and year

Health risk in traffic Number of fatalities/injuries in traffic per million hours intraffic

Accident risk Number of traffic accidents per million kilometres travelled perperson

Injury risk Number injured per million kilometres travelled per person

Death risk Number of fatalities per million kilometres travelled per person

Accident ratio Number of traffic accidents per million kilometres travelled pervehicle

Injury ratio Number injured per million kilometres travelled per vehicle

Death ratio Number of fatalities per million kilometres travelled per vehicle

Vehicle/accident ratio Number of vehicles involved in accidents per millionkilometres travelled per vehicle

Injury consequence ratio Number of injured per police reported accident

Death equivalence ratio Number of fatalities plus number of serious injuriesplus number of minor injuries

At the international level some of the more common benchmarks used for comparingtraffic safety in different countries include: the number of fatalities per 100,000 populationper year; the number of fatalities per 10,000 registered vehicles per year; and the numberof fatalities per one-billion vehicle kilometres travelled. Other important descriptiveaccident data includes socio-economic costs where each accident involving injury orfatality can be assigned a financial value based on the costs of medical treatment,property damage, use of emergency services, indirect costs associated with the loss ofproductivity following early death or during the period for recovery from injury, and costsrelated to other humane values.

Presently, the estimated value for the loss of a human life in road traffic in Sweden isestimated to be in excess of 1.2 million Euros (SRA, 2001). Values are also assigned toserious and minor injuries. These costs accumulate to a substantial amount each yearand form the basis for socio-economic calculations in relation to alternative roadway

designs and safety measures.

3.1.2 Predictive/Analytical Models

The main function of predictive models is to describe an experimental situation and themathematical relationship among independent and dependent variables. This is done inorder to investigate and predict how changes to independent variables might be reflectedin dependent variables. Predictive models are commonly used to estimate the effects ofspecific safety countermeasures and alternative roadway designs.

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Capacity calculation programs and other traffic planning tools are typical of this type ofmodel application. Predictive modelling is advantageous when there are a large number ofexperimental variables in combination with influences from various sources that are notexperimentally controlled. This approach is an alternative to the use of before-and-afterstudies that require an unrealistic and impractical data collection time-period.

In the OECD report on road safety principles and models, six types of independentvariables that influence accident frequency are mentioned, including:

Weather

Socio-economic factors

Transportation and infrastructure related factors

Data collection factors

Randomness

Countermeasure intervention

The effects of the first four of these can be greatly reduced through econometricmodelling. With regard to randomness, accidents are considered to include elements ofsystematic and stochastic variation where long-term accident frequency is the result of acausal process that is stable by virtue of the law of large numbers. Countermeasureintervention and the unexpected effects of actively planned safety measures represent amore complex phenomenon. Today, there are advanced multivariate probabilisticmethods based on cross-sectional studies (i.e. spatial variation) or time-series studies (i.e.temporal variation) that can be used for such purposes, although a great deal depends onthe choice of explanatory variables and probability laws and related distributions used.

Professional judgement is often required in order to ensure that the model is specifiedcorrectly, and that the relevant questions are answered.

The literature suggests that many models of this type are too rigid and lack a soundtheoretical foundation, which effectively restricts their predictive ability (Hakim et.al.,1991). Models that use individual utility-maximisation behaviour are thought to have thegreatest potential for a more flexible modelling approach in the future (Blomquist, 1986;Stewart,1998). New theoretical frameworks are needed, however, to improve safetyimpact estimation and demonstrate the cost-effectiveness of different intervention policies.

3.1.3 Risk Models

The majority of risk-factor models take a bottom-up approach to the traffic safety situation,as opposed to the top-down approach of the econometric models described previously.These models commonly focus on the individual risk related to driver behaviour. Here, thedriver is regarded as a single element as part of a complex and dynamic system. Themain aim of such models is to identify and quantify risk factors that explain and predictroad-user behaviour and to make safety assessments based of the risk-reduction effect ofvarious countermeasures. In models such as these, risk for accident involvement is oftenassumed to vary in accordance with measures situational (or cognitive) complexity.

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The OECD (1997) suggest two main approaches to risk analysis modelling: an analytical,system-oriented approach that aims at identifying risk factors and determining themechanisms that act on the occurrence and severity of accidents; and a quantitativeapproach that attempts to estimate different effects on the basis of risk calculations. Thefirst and most popular approach, tries to identify failures in the functioning of the traffic

system and thereby enhance risk. The quantitative (and less popular) approach entailsepidemiological studies in which particular cases with identified risk factors are followedand evaluated against control groups.

Risk assessment is not always a rational process and often involves complex decision-making tasks where objective risk can be compromised by subjective evaluation. Risk isassumed to represent a measure of danger that is evaluated by traffic system users in asubjective way. For those who manage the system risk is evaluated more objectively. Theanalysis of risk at the individual level is characterised by a large number of differentscientific disciplines.

At the traffic system level, risk management often involves statistical measures ofindividual and societal risk calculated as a general measure of the road traffic system.Determinants or factors relating to the components of this system can influence theprobability of an accident occurring. Comparing specific risk values with previouslydetermined rates can help to detect emerging safety problems. The identification,quantification and analysis of risk factors usually result in safety countermeasuresdesigned specifically to reduce accident risk and typically involve engineering, educationand enforcement.

There are a large number of action-oriented and risk-oriented models that exist at theindividual road-user level. These are mainly centred around the reliability of driverperformance and look at factors associated with driver behaviour. More technicallyoriented models also exist, although there are few that represent the driver in interactionwith other important elements in the traffic system. Integrative behavioural models alsoexist that deal with different behavioural phenomena. Michon (1989) has classified thesemodels into three main groups: input-output models, task-analysis (taxonomic) models,and functional models (which can be further subdivided into technical, mechanical,adaptive, motivational, or cognitive mechanisms). A review of some of the morecomprehensive models used in traffic psychology, and an analysis of the factors thatinfluence road-user behaviour can be found in Echterhoff (1991; 1992).

The OECD (1997) report suggests that the diversity of traffic safety related researchfindings at the individual level require a significantly more theoretical approach in order toaccurately interpret and systematically make use of the results. The application of suitablemodelling techniques enables the identification of important and influential individual riskfactors that can provide useful information to researchers and policy-makers for solvingtraffic safety problems.

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A problem with many microscopic risk models is that they are often too specific andcontext dependent, lacking consideration for other important traffic system elements. Thisis often the case with models from the social sciences that consider, for example, thequalitative relationship between individual psychological factors and accident involvementwithout specific interest in how risk levels are influenced (see e.g. Brehmer, 1993;

Williams Paek & Lund, 1995; Groeger & Rothengatter, 1998). Similarly, there are manyother perceptual and cognitive-based theories that focus on how information isascertained and processed, and how these processes are influenced by other internal andexternal factors with regard to different types of errors, and the physical limits that exist inrelation to various human processes (see e.g. Wickens, 1992, pp. 258-311, Englund et.al.1998, pp. 266-280).

3.1.4 Accident Consequence Models

Accident consequence models exist at both the individual (microscopic) level and theoperational traffic system (macroscopic) level. The main aim of this type of model is to

reduce the consequences of accidents by improving factors such as: the roadenvironment, vehicle safety, emergency services, or alternatively by promoting safetyequipment or influencing driver behaviour. At the macroscopic level, accidentconsequence models are able to provide a representative overview of the generalproblems associated with accident consequences. The data from reported accidents isoften used to categorise types of accidents in accordance with different factors such as:accident victims, vehicles, or situational characteristics.

In-depth analyses are often conducted in order to get as much detailed information aboutspecific accident types as possible. This type of analysis is usually qualitative in natureand sometimes do not result in general conclusions concerning how the safety situationcan be improved. The use of in-depth studies is, however, costly and time consuming, andpresently there is little data available at the international level from such analyses thatsuggests how the data can and should be used for safety countermeasure purposes.

In Sweden, in-depth road accident studies have been carried out since 1997 for allaccidents involving fatality. A special evaluation method (OLA) has also been establishedin order that system managers can make policy decisions concerning appropriatepreventive measures. The study of accident data from both aggregated statistics andspecific in-depth analyses of individual accidents has revealed a number of factors thatcan directly influence the consequences of road traffic accidents, including:

Mode of transport used by the accident victims. Type of accident and type of road and/or junction

Weather, road conditions, and lighting

Age of the accident victims.

Speed and mass of vehicles involved in an accident

Availability and speed of medical, and emergency services

Other factors related to vehicle engineering and/or roadway design

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Accident consequence models include the development and use of different injury scalessuch as the Abbreviated Injury Scale (AIS), an internationally recognised six-pointclassification scale. For cases involving multiple injuries, an overall injury severity score(ISS) exists. Long-term impairment or injury can be assessed using the Injury ImpairmentScale (IIS), Quality Adjusted Life Years (QALY) or Health Utility Index (HUI).

Focusing on accident consequences in relation to exposure allows for the effectiveness ofinjury reducing measures to be evaluated. Effectiveness evaluations in the form of before-and-after studies have, for example, revealed positive effects related to the use of seat-belts, crash helmets and speed reduction measures in terms of a reduction in thenumbers of injuries and fatalities.

In many countries, it is common practice to implement safety measures for accidentprevention based on the monetary values associated with accident consequences. This isa strategic socio-economic approach, which is often used by public policy makers toassess the potential benefits associated with accident prevention measures against their

relative investment costs.

Accident consequence approaches also include intervention strategies such as theintroduction of legislation and policies related to the control of speed, alcohol and drugs,use of seat belts and driver licensing. There is also legislation governing the design of theroadway including road signs and road markings to ensure standardisation and safety.Furthermore, there are laws that determine minimum safety standards for newlymanufactured vehicles, and the roadworthiness of older vehicles.

National or state level intervention can also take other forms. In some countries, large-scale publicity and educational programs have been successfully used to encourage safer

driving and reduce accidents. Furthermore, the costs associated with vehicle taxes, fueltaxes, and road-toll systems can be used indirectly to influence mobility and traffic safetyat the national level.

3.2 Levels of Abstraction in Safety Modelling

The different macroscopic and microscopic approaches to traffic safety are all ofimportance in order to establish an effective integrated traffic safety management strategyfor the future. Theories and models need to be developed from different perspectives andat different levels of abstraction in order to allow for the establishment of a largerconceptual framework that considers and protects both the needs of the individual road-

user and those of society in general.

The importance of the macroscopic approach lies in the ability to understand, explain andpredict variations in the traffic safety situation at the national level. This perspective ontraffic safety was perhaps first highlighted by the controversial proposition of Smeeds lawin 1949. This law suggested that the risk of being involved in an accident diminished withincreasing motorisation. Researchers including Brde (1996) and Oppe (1989), havesince found more stable patterns of traffic safety development.

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Smeed also suggested that the aspects of traffic safety that are most prominent at anyone particular point in time are dependent on the result of temporary and complexrelationships determined by the dynamic forces existing in the current political, social, andeconomic climate. An observation that is very relevant at the present time.

The diverse findings of traffic safety research at the microscopic level suggest the need toestablish a coordinated multi-disciplinary foundation on which future research can bebased. Perhaps one of the biggest problems associated with traffic safety research is thelack of congruency between models and theories at different levels of abstraction (i.e.those at the macroscopic and microscopic level). The reason for these conceptualdifferences is believed to be attributable to the way in which top-down and bottom-upresearch is conducted and the different aims and goals of the two approaches.

The OECD (1997) suggests the need for theoretical research at all levels in order toovercome some of these problems. The use of meta-analysis has become increasingpopular among traffic researchers in order to make qualified predictions about the impact

of different safety measures (see e.g. Elvik, 1995). The accuracy and predictive quality ofthis type of research is, however, greatly dependent on the quality of the individual studiesthat are selected, and their reliability, validity and relevance to the research problem.

3.3 Traffic System Simulation and Modelling

In the field of traffic planning and engineering today, simulation modelling is a key part ofthe analysis process. Traffic simulation models are designed to represent the behaviour ofthe traffic system where individual entity behaviours and interactions are integrated toproduce a quantitative description of system performance. These models aremathematical/logical abstractions of the real world. Simulation has become an effective

tool for analyzing a wide variety of complex and dynamical problems that cannot bestudied accurately and adequately by other analytical means. There are great manydifferent simulation models and tools that can be used to describe, analyze and predicteffects in large or small-scale networks or isolated streets and intersections.

According to Lieberman and Rathi (1997), traffic simulation can be used for a largenumber of traffic engineering and transportation planning tasks including: the evaluation ofalternative treatments, the test and visualization of new designs, as an important part ofthe design process, as an integrated part of other traffic analysis tools, for the training ofpersonnel (e.g. traffic control centres), and for safety analysis.

Lieberman and Rathi also state that simulation should not be used in situations wheremathematical approaches are feasible given the spatial and temporal scale of the model.Similarly, the authors state that traffic simulation should not be used instead ofoptimization models, capacity estimation procedures, demand modelling and designactivities but rather as a method to support this type of work and as a tool for visualisingthe traffic system. This helps users to gain an understanding for how the system functionsand behaves under different conditions. Simulation models represent a means to describethe dynamical processes of the traffic system and explain the resulting statistics.

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3.3.2 Classifying Simulation Models

There are a number of ways in which simulation models can be categorized. While it istheoretically possible to have continuous models that describe how the elements of amodel change over time in response to continuous stimuli, discrete time-based modelsare more common. In time-based models, time is segmented into a succession of knownintervals. During each interval, the activities that change the states of selected systemelements are computed. An alternative to discrete time, is the use of discrete events.Discrete event based models respond to changes in state and are generally moreeconomical than those that are time based if the system size is limited and if the states ofthe entities change infrequently.

The most common classification terminology refers to the level of detail that each systemrepresents (for a more detailed discussion regarding different levels of traffic networkmodelling see Merritt, 2003). The three levels of modelling recognized in the majority ofsimulation software tools include:

Macroscopic models (low fidelity)

Mesoscopic models (mixed fidelity)

Microscopic models (high fidelity)

Macroscopic models describe entities and their activities and interactions at a relativelylow level of detail. The performance of a link is often represented by a function determinedfrom link attributes (e.g. number of lanes, length, etc.) and established relationshipsbetween speed, flow and density. Arguably, macroscopic models should be used whenthe intended results are not sensitive to microscopic detail, or where the scale of themodel is such that the execution time would be unacceptable with a higher level of detail.

Mesoscopic models generally represent dynamic system entities at a relatively high levelof detail, but describe the activity of such entities at far lower lever of detail thanmicroscopic models. There are also a number of simulation tools that, for practicalreasons operate at a level between the macroscopic and mesoscopic.

Microscopic models describe entities and activities at a high-level of detail. The actuallevel of detail can vary from one software package to another. In these models, theperformance of individual entities is represented by a number of smaller sub-models thatdescribe elementary behaviour such as car following, gap-acceptance or lane changing. Itis also possible to represent the variation in behaviour and performance among road-

users through these and other sub-models. Vehicle performance such as braking andacceleration rat

archer 2004 b

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