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Data Requirements for Network Screening and Large Scale Evaluation Studies

Data Requirements for Network Screening and Large Scale Evaluation Studies. Calvin J. Mollett, M(Eng) and Geni Bahar, P.Eng. Presentation Outline. Network Screening Safety Evaluations Safety Performance Functions Data Requirements Data Format and Quality Issues. Network Screening.

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Data Requirements for Network Screening and Large Scale Evaluation Studies

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  1. Data Requirements for Network Screening and Large Scale Evaluation Studies Calvin J. Mollett, M(Eng) and Geni Bahar, P.Eng.

  2. Presentation Outline • Network Screening • Safety Evaluations • Safety Performance Functions • Data Requirements • Data Format and Quality Issues

  3. Network Screening Objective: • To identify sites with a potential for cost effective improvements Conventional screening methods: • Crash counts • Crash rates • Are inefficient

  4. Network Screening Inefficiencies: • FALSE Positives • Sites incorrectly identified as unsafe • Waste resources • FALSE Negatives • Sites incorrectly identified as safe • Unsafe locations remain untreated

  5. Network Screening WHY ? • Randomness in crash counts • Crash counts can fluctuate up or down for no apparent reason (not related to safety) • Often site is identified during random upward fluctuation

  6. Network Screening The SOLUTION ? • Screening based on potential for safety improvement: • Expected crash frequency • Excess number of crashes

  7. Network Screening Expected crash frequency Predicted Observed Expected Observed Expected Predicted B A

  8. Crash database Bayes Theorem Safety Performance Functions Network Screening Excess crashes Observed Expected Predicted

  9. Safety Evaluation Objective: • How effective has a measure been in reducing crashes ? • Cost-effectiveness of measure • Feasibility for future implementation

  10. Determine number of expected crashes – if measure was not implemented Determine actual number of crashes – after implementation of measure Safety Effect Safety Evaluation Procedure

  11. Safety Evaluation • How to determine expected number of crashes? • Conventional before-after method • Average crash count during before period • Unreliable due to regression-to-the-mean (RTM)

  12. Safety Evaluation • Regression-to-mean

  13. Safety Evaluation • Hauer (1997) • Method to estimate the expected number of crashes during the after period • SPFs (for untreated condition) • Historical crash counts • Actual traffic volumes

  14. Safety Performance Functions • Crash prediction models • Two different types • Level 1 • E(Ki) = i(AADTi) • Level 2 • E(Ki) = i(AADTi)1exp(2shldwidth)

  15. Safety Performance Functions Data requirements: • Reference group • Crash data • Roadway attribute data • Traffic volume data

  16. Data Requirements Traffic Volumes: • Each year of study period • Missing volumes to be estimated • Links • AADT (both directions) • Intersections • Approach AADT on each intersection

  17. Data Requirements Crash data: • Minimum requirements • Location (accurate to within 0.1 mile) • Severity (Fatal, Injury, Property Damage) • Date • Before : 3 – 5 years • After : 1 Year minimum • Safety evaluations – identify target crashes

  18. Data Requirements Roadway attribute data: • Identify reference groups (e.g 4 Lane Divided Urban Freeways) • To develop Level 2 (FULL) models

  19. Data Requirements Treatment Details: • List of treatment locations • Exact position - e,g route, begin mile point, end mile point • Date/Year of implementation

  20. Data Format and Quality • Databases • Location Referencing • Roadway attribute data • Traffic volumes • Crash data

  21. Data Format and Quality Databases: • All data – relational database files • All files should be linkable – common referencing system • Sufficiency Files • Flat file • Combination of different files • Created annually

  22. Data Format and Quality Location referencing system: • Ideal: • Route, Milepost • Also: • Route, Segment, Offset • Route, Reference point, Offset • Route, Reference marker • Require conversion during data preparation

  23. Data Format and Quality Roadway attributes: • Each record – unique identifier • Route number, mile points • Intersection identification code • Create sufficiency files • Consistency with crash data file

  24. Data Format and Quality Traffic Volumes: • Traffic volumes between intersections • Volume estimation procedures • Development of annual counting programs

  25. Data Format and Quality Crash Data: • Crash code changes - updating of historical data • Accuracy of crash location to 0.1 mile • Consistency between time of crash and ambient light • Consistency between Impact type and vehicle directions • Uniformity between State databases - MUCC

  26. Conclusion • Data is a valuable resource • Questions: • Do you have the data? • Are data of the right format and quality? • Upcoming safety tools • SafetyAnalyst • Highway Safety Manual

  27. Thank You

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