Colorado Department of Transportation Crash Data Program Alisa Babler, PE

Transcription

1 Colorado Department of Transportation Crash Data Program Alisa Babler, PE

2 2014 Colorado 115,388 Total Crashes 39,353 Injuries 488 Fatalities MVMT 88,258 Roadway Center Line Miles

3 Media Question: What is the worst highway? Total crashes? Severe crashes? Total crash rate? Severe crash rate? Level of Service of Safety? It depends..

4 Measuring Safety Worst/Best? Similar Highway Sections

5 Measuring Safety Total Crashes

6 Measuring Safety Inj and Fatal Crashes

7 Measuring Safety Total Crash Rate

8 Measuring Safety INJ+FAT LOSS

9 Measuring Safety Total LOSS

10 Why not Dots on a Map Dots don t tell the whole story Dots don t consider volume Dots don t consider roadway design Dots don t consider predicted norms of crashes Dots don t account for severity Dots don t address the perception that more crashes are always worse

11 Fatal Dot s on the Map

12 Safety Performance Function (SPF) Predictive models for specific classes of roadway Reflects the relationship between traffic exposure measured in ADT, and crash count for a unit of road section measured in crashes/mile/year Provides an estimate of the expected crash frequency and severity for a range of ADT

13 SPF Example Urban 6-Lane Freeway APMPY ,000 AADT Corresponds to An Expected Accident Frequency of 60 Accidents Per Mile Per Year 0 20,000 40,000 60,000 80, , , , , , ,000 AADT

14 Level of Service of Safety (LOSS) Describes the degree of safety or un-safety of a roadway segment Communicates the magnitude of the safety problem Aligns perception of roadway safety with the reality Provides a frame of reference for decision making on other projects (resurfacing or reconstruction) Provides a frame of reference for planning Employs Empirical Bayes method

15 Level of Service of Safety LOSS reflects how the roadway segment is performing in regard to its expected crash frequency and severity at a specific level of ADT. LOSS IV LOSS III LOSS II LOSS I

16 SPF Tools SPF Models: Total Frequency, and Severity (INJ+FAT) LOSS: 4 Levels LOSS-I: Indicates a low potential for crash reduction LOSS-II: Indicates a better than expected safety performance LOSS-III: Indicates a less than expected safety performance LOSS-IV: Indicates a high potential for crash reduction

17 Direct Diagnostics - SH 88

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19 Fixed Object 1 1% Sideswipe (Same) 6 6% Broadside 9 9% Approach Turn 54 52% Rear End 33 32%

20 Direct Diagnostics (Predicted) 70.50% 29.32% 0.17% 5.16% 80.32% 14.29% 96.33% 0.35% 12.61% 0.44% 48.86% 9.20% 0.36% 20.10% 1.31% PDO INJ FAT Single Vehicle Accidents Two Vehicle Accidents Three or more Vehicle Accident On Road Overturning Broadside Head On Rear End Sideswipe (Same Direction) Sideswipe (Opposite Direction) Approach Turn Overtaking Turn

21 Cumulative Accidents by Date 60 Cumulative Accidents Accidents Accidents Protected Left Turn Arrow Installed in Early /01/97 01/01/98 01/01/99 01/01/00 01/01/01 01/01/02 01/01/03 01/01/04 Date

22 Pattern Recognition compares normative percentages of different crash parameters for highway segments Direct Diagnostics focuses on intersections or a single point on a road and compares those particular normative averages to identify patterns

23 SPF Models Highway models are custom, use Colorado data Accounts for local driving behavior Accounts for local conditions (weather) Models allow comparison with comparable design and volumes Rural mountains highways are not compared to urban, high volume interstates

24 SPF and Pattern Tools Mapped statewide Level of Service of Safety Mapped statewide patterns Available to CDOT staff for planning and project development

25 LOSS

26 INJ Pattern

27 Challenges Need sufficient mileage of each roadway type to develop effective models Non highway models haven t been developed Non highway location data isn t consistent statewide Requires some explanation to understand May not be the right tool for every use, such as operations

28 How is this being used? Provides specific locations for safety projects Provides realistic information for planners and decision makers Developed performance measures Used in project selection and scoping HSIP project evaluation Law enforcement staffing and deployment planning

29 QUESTIONS?

30 Alisa Babler, PE Data Unit Manager Colorado Department of Transportation TSM&O, Traffic Data Unit