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  • 1. IPISE Int. Communication,Understanding and Predicting Crash Risks on Freeways January 23, 2008 Moinul Hossain Dept. of Built Environment Tokyo Institute of Technology (TIT) Supervised By: Dr. YASUNORI MUROMACHI
  • 2. OutlineRoad Accidents – How Big is the Problem? Components of Road Accidents Safe Roads and Accident Prediction Accident Prediction Models – Progress Problems and My Research Objectives Methodology Expected Outcome Me, Myself and Road Safety
  • 3. Facts about Global Road Safety• 1.2 million die a year• 20-50 million more are injured or disabled• 11th leading cause of death• account for 2.1% of all deaths globally
  • 4. In Thailand, every day approximately 50 Thaipeople who leave home to work, school, shop,temple/church/mosque, social gathering neverreturn home because of road accidents. Source: Yordphol TANABORIBOON
  • 5. Road Traffic Fatality Ratein Several Countries in 1998Fatality Rate(per 100,000 population)50 Thailand Japan France German Sweden Britain Canada USA40 30.43020 14.4 15.4 9.5 10.0 8.610 6.0 6.1 0
  • 6. What are the Causes ofAccidents ?
  • 7. Components of CrashComponents of Crash Human HUMAN 57% 6% 27% 3% 2% 1% 3% Road & Vehicle Environment Source: Treat et al. (1980) Human 65% VEHICLE 4% 24% 1% 2% 1% 2% Road &ROAD & Vehicle EnvironmentENVIRONMENT Source: Sabey (1980)
  • 8. A Thai Experience ...A Thai Experience ... • Numerical Share Remains almost Identical to the Previous Studies • 20% of the Human Factors were found to be directly related to Road & Environment • 75% of the Human Factors were Directly or Indirectly related to Road & EnvironmentSource: Thailand Accident Research Center (2007)
  • 9. Pre-crash, Crash, Post-crash Pre-crash, Crash, Post-crash Factors Factors Road & Human Vehicle EnvironmentInformation Roadworthiness Road design & layoutAttitude Lighting Speed LimitImpairment Braking Pedestrian FacilitiesEnforcement Handling Speed ManagementUse of Restraints Occupant Restraints Crash protective roadsideImpairment Other Safety Devices objects Crash-protective designFirst-aid skill Ease of access Rescue facilitiesAccess to medics Fire risk Congestion Source: WHO world Report on Road Traffic Injury, 2004
  • 10. What solution can be offeredto improve the safetyproblems within a short timespan ?
  • 11. Accident Prediction Model (APM) A Potential Solution “Mathematical Models Capable of Predictingthe Safety Hazards of Roads based on Traffic Flow Variables and Other Physical and/or Environmental Variables.”
  • 12. Benefits of Accident Prediction Models Identify Factors and Establishing relationships between crashes and Explanatory Variables Eliminate Black-Spots from Future Road Projects Conduct Cost-Benefit Analysis for New and Existing Road Projects Understanding Change in Traffic Parameters in Pre & Post Crash Situations Predict Accidents in Real-time and Facilitate Proactive Safety Measures A POWERFUL TOOL WITH MULTIFARIOUS BENEFITS ...
  • 13. APMs - Classification Will not have any change in probability of accidents based on the change in time, lighting conditions, rainfall or other environmental and seasonal ct a S parameters that may vary in different parts of a day i t and different parts of the year. Will demonstrate varying probability of accident based on time and environmental parameters, however, will not depend on the input of real time traffic data.c m ny Di a Will take real time traffic data as an input to the model and calculate the risk of accidents in real time – a pro active approach. ae R
  • 14. Static ModelsVariables• Road Geometry (no. of lanes,Shoulders, Divider, SuperElevation, Curvature, etc.) Model• Pavement Characteristics • Generalized Linear Modeling• Type of Road (Poisson / Negative Binomial)• Severity of Accident • Bayesian Approach (Recent)• AADT• % of Heavy Vehicles, etc.
  • 15. Dynamic Models Variables Model• Road Geometry (no. of lanes, • Time Series Analysis (CalenderShoulders, Divider, Super Data Analysis)Elevation, Curvature, etc.)• Pavement Characteristics• Type of Road• Severity of Accident• AADT• % of Heavy Vehicles, etc.• Seasonal Input (e.g., Rainfall)
  • 16. Comments Static Dynamic• Based on Historical Data • Based on Historical Data• Concentrated Towards • Concentrated Towards Physical Characteristics Seasonal Variation• Ignores Seasonal Variation • Includes Trends and Weather• Explicit Theoretical Foundation Characteristics• Bayesian Approach provides • Explicit Theoretical Foundation superior outcome for low • Some Studies suggest that it sample size and low sample can provide better outcome mean than the Regression Models• Data requirements are • A Suitable Alternative relatively low approach when AADT, % HV, etc. are not readily available
  • 17. Real-time ModelsWhy is a Real-time Model Necessary???• Likelihood of a crash or crashprobability is vastly affected by theshort term turbulence of traffic flow How is it Different??? • Data Requirements are Different • A Proactive Measure • Can be linked with Real-timePrevention is Better than Cure... Accident Detection and ATIS
  • 18. Real-time ModelsVariables Data & Collection Method• Traffic Related Variables • Real-time Traffic Data(Traffic Flow, Speed, Variation of Collected with Single-LoopSpeed in Lanes, Standard DetectorDeviation of Speed and Volume, • Accident Data Collected fromVehicle Occupancy, Mean Appropriate AuthoritySpeed, Density, Speed • Weather Data Collected fromDifference between Upstream Meteorological Departmentand Downstream, etc.)• Seasonal Input (e.g., Rainfall) Prominent Studies Oh et al, (2000) Golob et al, (2002) Lee et al, (2002) Abdel-Aty et al, (2004) Garber et al, (2006)
  • 19. PROBLEMS• Interaction among Traffic, Geometric and Environmental Parameters were not studied• Loop detectors proved to be inefficient in providing good data for the pre and post crash situations• Different sources of data created a time lag between the actual time of accident and reported time of accident• No comparison was made between the performance of Real-time and non- Real-time models
  • 20. Research Demand and QuestionsResearch Questions What are the crash precursors or variables that canexplain the probability of accidents in real time? Can a traffic condition in relation to environmentalconditions be anticipated as dangerous enough to leadto crash? If yes then how? What are the performance variations among astatic, dynamic and a real time accident predictionmodels?
  • 21. Research Purpose The purpose of this research study is to understand the dynamics of road accidents from traffic engineering perspective through historical and pre and post crash condition study and devise a way to improve the roads as well as driving conditions to make drivers less susceptible to mistakes.
  • 22. Research Objectives Understand relationships of speed, flow, densityand environmental variables (e.g., weather, time ofday, etc.) during pre and post crash conditions anddevelop a real time accident prediction modelthereby. Develop static and dynamic accident predictionmodels as well for the same study area and comparethe prediction capabilities of these three models.
  • 23. Work Flow Diagram Literature Review (Prev. Study, Stat., TE, Tech.) Selection of Study Area Data Collection(Historical, Meteorological, Real Time – 3 to 4 months) Model Development Performance Measure and Comparison
  • 24. STUDY AREA STUDY AREAStudy AreaStudy Area• Country : Thailand• Highway : Route No. 9• Length of Section : 6-10 KmData SourcesData Sources• Historical : Dept. of Highways• Geometry : Dept. of Highways• Weather : Dept. of Meteorology• Real-time : Self
  • 25. REQUIREMENTS...• Equipment: – Video Cameras: 7-10, for 3-4 Months – Autoscope: 1, for 3-4 Months – Rain gauge: 7-10, for 3-4 Months Cooperation: – TARC – DOH – DOHP
  • 26. Limitations... The Model will not considerintersections or Entrances or Exits A Highly Accident prone section will be selected Data will be collected for a short period of Time Land Use Data will not be Considered The Model will not be Tested in Real-time situation
  • 27. Prepared by Yordphol T., AIT, 1 Dec 2005Thank YouWorking for road safety means not working under lime light ... even not being paid attention by other people. - Late Professor Yordphol TANABORIBOON. First Manager of Thailand Accident Research Center
  • 28. Diagrams can be DECEPTIVE . . . •High Concentration on Human Factors by the Decision Makers •Improvement through Human Factors demands uncertain Time-limit A Substantial Portion of•the Human Factors can be eliminated with betterdriving Condition•Concentration of Road and Environment mayremain unattended.
  • 29. The Million Dollar Question: Academic and Practical Contribution...Academic Contribution Practical Contribution•Understand relationships • Develop a proactivebetween crash and traffic accident Warning systemvariables considering • Calculate Cost ofenvironmental variations Accident or Delay with•Comparison among Static, Higher accuracyDynamic and Real-time • Use VMS and VSL withAccident Prediction Models higher effectiveness• A New method ofdeveloping real-timeaccident prediction models(hopefully...)
  • 30. Me, Myself andRoad Safety
  • 31. Projects, Reports and Trainings (in Road Safety Only)Projects Team Leader, Nationwide Real-time Vehicle Tracking System, Monico Limited, Bangladesh. July 2007 – September 2007. (For Profit Project). Research Associate, Thailand Accident Research Center (TARC), Thailand. June 2006 – July 2007. System Analyst, Provincial Road Safety Management System (PRSS), Donor: Thai Health Promotion Foundation, Thailand. June 2006 – December 2008. Student Assistant, Thailand Accident Research Center,Thailand. June 2005 – May 2006.Project Reports Project Reports and Business Plan of Thailand Accidents Research Center, Thailand Application manual of Provincial Road Safety Management System (PRSS), ThailandTraining Lecturer, Road Safety and IT, The Third Country Training on Injury Surveillance (TCTP), November 14, 2006, Khon Kaen Hospital, Khon Kaen, Thailand. Donor: JICA.
  • 32. Publications (in Road Safety Only...)Thesis, Master of Engineering Application of Data Mining in Road Safety, AIT Thesis, 2006, Thailand.Research Papers Motorcycle Accidents, Alcohol Intoxication and Futile Helmet Legislation in Thailand: How Long Must We Tolerate. Journal of the Eastern Asian Society for Transportation Studies, Vol. 7, China, October 2007. Medical Investigation of Motorcycle Accidents in Thailand. Journal of the Eastern AsianSociety for Transportation Studies, Vol. 7, p. 147-162, , China, October 2007 Efficacy of Safety Helmets for the Thai Motorcyclists. The 3rd National Transport Conference: Sustainable and Safe Transportation Systems (SSTS), Khon Kaen University, 24 November,2006, Khon Kaen, Thailand. Paper ID: TS-08. Probability of Survival (PS): An Alternative Severity Assessment Approach in Road Safety, Proceedings of International Conference of Road Safety in Developing Countries, AccidentResearch Center, Bangladesh University of Engineering and Technology, 22-24 August, 2006,Dhaka, Bangladesh. A Framework of Injury Surveillance (IS) Database for the Developing Countries: A Thai Experience from the Road Safety Perspective, Proceedings of International Conference ofRoad Safety in Developing Countries, Accident Research Center, Bangladesh University ofEngineering and Technology, 22-24 August, 2006, Dhaka, Bangladesh. A Study on Pedestrian Accidents Based on the Injury Surveillance (IS) Data: Thailand’s Case,
  • 33. Before September 11, 2001 September 11, 2001
  • 34. Benefits of Accident Prediction ModelsIdentify Factors and Conduct Cost-BenefitEstablishing relationships Analysis for New andbetween crashes and Existing Road ProjectsExplanatory VariablesUnderstanding Change Predict Accidentsin Traffic Parameters in APM in Real-time and Facilitate ProactivePre & Post CrashSituations Safety MeasuresMay be used as an Predict Seasonal Eliminate Black-input in Road and Env. Variation Spots from FutureMaintenance Projects of Accidents Road Projects A POWERFUL TOOL WITH MULTIFARIOUS BENEFITS ...
  • 35. Previous Models at a Glance
  • 36. Research Demand and QuestionsDemand:  Traffic and environmental data require to be obtained in a more accurate way  Time of accident need to be detected accuratelyResearch Questions What are the crash precursors or variables that canexplain the probability of accidents in real time? Can a traffic condition in relation to environmentalconditions be anticipated as dangerous enough to leadto crash? If yes then how? What are the performance variations among astatic, dynamic and a real time accident predictionmodels?