Taking a Proactive Approach to Safety in Utah and Calibration of the HSM SPFs

1. Taking a Proactive Approach to Safety in Utah and Calibration of the HSM SPFs 2012 Western District Annual Meeting June 27, 2012 Session 9A 10:30 – 12:00 p.m. Grant G. Schultz, Ph.D., P.E., PTOE Associate Professor, Department of Civil & Environmental Engineering Brad Brimley, E.I.T., and Steven Dudley, E.I.T. Graduate Research Assistants Mitsuru Saito, Ph.D., P.E. Professor, Department of Civil & Environmental Engineering

2. Outline • Introduction • Fundamentals of Highway Safety Analysis • Framework for Highway Safety Mitigation • Conclusions and Recommendations

3. Introduction • Utah highway safety statistics • Need for integration of safety • Highway Safety Manual (HSM)

4. Utah Highway Safety Statistics • Total Reported Crashes: 56,367 (2008)

5. Need for Integration of Safety • Safety has long been an important consideration in transportation decision making • SAFETEA-LU (2005): • Highway Safety Improvement Program (HSIP) seeks to reduce the number of fatal and injury crashes through state-level engineering methods • Decision makers need good methods and tools to make optimal decisions to improve safety

6. Highway Safety Manual (HSM) • Published in 2010 by AASHTO • Purpose: • Integrates safety into the decision-making processes • Science-based • Provides information and tools • No legal obligation • Updated with recent research

7. Fundamentals of Highway Safety Analysis • Crash data limitations • Descriptive statistics • Predictive analysis

8. Crash Data Limitations • Randomness and change • Regression to the mean (RTM) bias

9. Descriptive Analysis • Based on historical crash data • Traditional methods: • Crash Rate = # of crashes / exposure • Crash Frequency = # of crashes / period of years • Fails to account for RTM

10. Predictive Analysis • Crash Prediction: • Safety Performance Function (SPF): • Example: Nspf = (AADT) X (L) X (365) X (10-6) X (e-0.312) • Crash Modification Factor (CMF): • CMF = Crashes condition A / Crashes condition B

11. Predictive Analysis • Statistical Methods: • Empirical Bayes Method • Hierarchical Bayes Method • Bayesian methods use: • Prior knowledge – history of similar sites • Likelihood of certain events • Bayes theorem for degree of uncertainty

12. Highway Safety Mitigation • Step 1: Network Screening • Step 2: Diagnosis • Step 3: Countermeasure Selection • Step 4: Economic Appraisal • Step 5: Project Prioritization • Step 6: Effectiveness Evaluation

13. Highway Safety Mitigation • Step 1: Network Screening • Step 2: Diagnosis • Step 3: Countermeasure Selection • Step 4: Economic Appraisal • Step 5: Project Prioritization • Step 6: Effectiveness Evaluation

14. Step 1: Network Screening • Purpose: • Identify sites that can benefit most from safety improvement

15. Step 1: Network Screening • Process: • Establish Focus • Identify Network and Reference Populations • Select Performance Measures: • Traditional vs. Bayesian • Select Screening Method: • Sliding window, peak searching, simple ranking • Screen and Evaluate Results

16. Network Screening Example • Introduction • Purpose and need • Data collection • Network screening analysis • Network screening results

17. Network Screening ExampleIntroduction • SPFs: two primary uses: • Predict safety • Evaluate previous performance • SPFs incorporate known information about a roadway: • AADT • Roadway conditions • Geometry

18. Network Screening ExampleIntroduction • SPFs in the HSM: • Rural two-lane two-way roads • Rural multilane highways • Urban and suburban arterials • Rural two-lane two-way SPF:Nspf = AADT × L × 365 × 10-6 × e-0.312 • Number of crashes ∝ MVMT • Other factors are multiplied forcertain roadway characteristics

19. Network Screening ExamplePurpose and Need • SPFs account for components that affect roadway safety • AASHTO recommends agencies calibrate the SPFs in the HSM to fit local conditions or develop new models • Goal: find the “best” model for UDOT to use in the network screening process

20. Network Screening ExampleData Collection • Objective: Obtain as much data as reasonably possible: • Homogeneous, tangent roadway segments • Lane width • Shoulder width • Lane striping • Rumble strips • Speed limit • Driveways • Longitudinal grade • AADT (2005-2007) • Percentage of trucks (2005-2007) • Total crashes (2005-2007)

21. Network Screening ExampleData Collection • Data Origins: • Roadview • Google Earth • UDOT AADT Tables • Construction Tables • Crash Database

23. 157 study segments

24. Network Screening ExampleData Collection Descriptive Statistics for Data

25. Network Screening ExampleData Collection Yearly Variations in Data

26. Network Screening ExampleAnalysis • Model building: • Calibration factor: C = Nobs/NHSM • Neg. binomial models: ln(N) = β0 + β1x1+β2x2… • Created with SAS using backward stepwise regression • 2 models with original data (75% and 95% confidence) • 2 models with natural log transformation of AADT (75% and 95% confidence)

27. Network Screening ExampleAnalysis • Model building (con’t): • Hierarchical Bayesian model: • Variable coefficient is a distribution rather than a single point • Able to account for variability in data • Created with R using the same model form as negative binomial models • Same variables as 4th negative binomial model

28. Network Screening ExampleResults • HSM Calibration: • 426 reported crashes; HSM predicts 368 • Calibration factor: 1.16 • Calibrated HSM model:Nspf = 1.16 × AADT × L × 365 × 10-6 × e-0.312

29. Results • 1st new model: • Original data • 75% confidence N = exp[-7.49 + (0.0002)(AADT) + (0.429)(L) + (0.0286)(DD) + Pass – (0.268)(SRS) – (0.0219)(CT) + (0.1036)(Speed)]

30. Results • 2nd new model: • Original data • 95% confidence N = exp[-7.17 + (0.0003)(AADT) + (0.423)(L) + Pass – (0.0219)(CT) + (0.0938)(Speed)]

31. Results • 3rd new model: • Natural log AADT • 75% confidence N = AADT 0.753 × exp[-12.1 + (0.442)(L) + (-0.0498)(SW) + (Pass) + (0.0277)(DD) + SRS – (0.0257)(CT) + (0.101)(Speed)]

32. Results • 4th new model • Natural log AADT • 95% confidence N = AADT 0.840 + exp[-12.1 + (0.450)(L)– (0.0271)(CT) + (0.0824)(Speed)]

33. Network Screening ExampleResults • Hierarchical Bayesian model: • Variables: • Segment length • ln(AADT) • Combo truck percentage • Speed limit

34. Network Screening ExampleResults • Determining “hot-spots” with hierarchical Bayesian model: Segment Characteristics: Length: 1.2 mi AADT: 1254 veh/day Combo Trucks: 9.3% Speed Limit: 65 mph Observed Crashes: 8 Prediction density Actual crashes

35. Highway Safety Mitigation • Step 1: Network Screening • Step 2: Diagnosis • Step 3: Countermeasure Selection • Step 4: Economic Appraisal • Step 5: Project Prioritization • Step 6: Effectiveness Evaluation

36. Step 2: Diagnosis • Purpose: • Better understand crash patterns and identify the factors contributing to crashes • Process: • Review safety data • Assess supporting documentation • Examine field conditions

38. Example Collision Diagram

39. ExampleConditionDiagram

40. Example Hadon Matrix: Rear-end Crash

41. Highway Safety Mitigation • Step 1: Network Screening • Step 2: Diagnosis • Step 3: Countermeasure Selection • Step 4: Economic Appraisal • Step 5: Project Prioritization • Step 6: Effectiveness Evaluation

42. Step 3: Select Countermeasures • Purpose: • Identify possible countermeasures that will reduce crash frequency or crash severity • Process: • Identify factors contributing to crashes • Identify potential countermeasures • Select preferred treatment based on economic analysis

43. Example Countermeasures • Rear-end collisions: • Right/left turn bays • Realign road for better sight distance • Advanced warning lights • Run-off the road and rollover crashes: • Expand clear zone, flatten side slope • Rumble strips and lane markings • Construct more rest stops

44. Highway Safety Mitigation • Step 1: Network Screening • Step 2: Diagnosis • Step 3: Countermeasure Selection • Step 4: Economic Appraisal • Step 5: Project Prioritization • Step 6: Effectiveness Evaluation

45. Step 4: Economic Appraisal • Purpose: • Determine if a safety improvement project is economically valid by comparing benefits and costs of the project • Process: • Assess benefits of project • Estimate project cost • Apply Economic Evaluation Methods

46. Assessing Benefits and Costs • Determine change in expected average crash frequency • Convert benefitsto presentmonetary value: • benefits = crashesreduced x societal costs

47. Apply Economic Methods • Net Present Value (NPV): • Economically valid if NPV ≥ Zero • NPV = PVbenefits – PVcosts • Benefit-Cost Ratio (BCR): • Economically valid if BCR ≥ 1.0 • BCR = PVbenefits / PVcosts • Cost-Effectiveness Index: • Cost per crash reduced • CEI = PVcosts / Δ Crash Frequency

48. Highway Safety Mitigation • Step 1: Network Screening • Step 2: Diagnosis • Step 3: Countermeasure Selection • Step 4: Economic Appraisal • Step 5: Project Prioritization • Step 6: Effectiveness Evaluation

49. Step 5: Prioritize Projects • Purpose: • Organize potential projects into an ordered list that will maximize the benefit from highway safety investment • Process: • Rank sites by economic or benefit-cost criteria • Optimize projects to fit within budget constraints

50. Highway Safety Mitigation • Step 1: Network Screening • Step 2: Diagnosis • Step 3: Countermeasure Selection • Step 4: Economic Appraisal • Step 5: Project Prioritization • Step 6: Effectiveness Evaluation