1. Evaluating the performance of three different network screening methods for detecting high collision concentration locations using empirical data Prepared by Koohong Chung, Ph.D., P.E. California Department of Transportation

2. Outline 1. Motivation and Background 2. Methods 3. Findings 4. Concluding remarks 5. Q & A

3. Motivation and Background

4. - 50,000 miles of highway and freeway lanes. California Department of Transportation (Caltrans) - 58 Counties - 480 cities Motivation and Background - 163,695 square miles 37,691,912 - 37 million people live in California

5. Caltrans Funding from multiple sources STIP TSM SHOPP Toll Bridge Program (State Transportation Improvement Program ) (Traffic System Management) (State Highway Operation and Protection Program) $11.2 billion Motivation and Background

6. Caltrans Funding from multiple sources STIP TSM SHOPP Toll Bridge Program (State Transportation Improvement Program ) (Traffic System Management) (State Highway Operation and Protection Program) $11.2 billion $2 billion Motivation and Background

7. SHOPP(State Highway Operation and Protection Program) - For 2012~2013 fiscal year, the program needs $7.4 billion, but will be receiving only $2 billion. - SHOPP has several sub categories (there are 9 of them) and one of them is “Collision Reduction” and $346 million has been allocated to this category. - Under Collision Reduction program, sites identified as HCCL locations by Sliding Moving Window (SMW) approach are investigated to be considered for the implementation of safety counter measure. Motivation and Background

8. 8 “Sliding Moving Window” Approach 0.2 mile roadway the reference value the number of collisions with the window Motivation and Background

9. 9 “Sliding Moving Window” Approach 0.2 mile roadway the reference value slide the window by small increments of 0.01 mile and repeat the same analysis 0.01 mile the number of collisions with the window < Motivation and Background

10. 10 “Sliding Moving Window” Approach 0.2 mile roadway The site will be reported to Table C or Wet Table C and move the window to the next 0.2 mile segment the reference value the number of collisions with the window > Motivation and Background

13. 13 the reference value the number of collisions with the window > Freeway 4 Lanes or Less Urban Motivation and Background

14. 14 Survey Result (2002, Table C Task Force Report) I. Adjacent sites are often identified II. High false positive rate (i.e., requiring sites for safety investigation when it is not needed) Motivation and Background

15. 15 Develop hot spot identification method that reduces false positives without increasing false negatives Motivation and Background

16. 16 To detect HCCL, you need the following information: 1. Traffic collision data 3. Network screening procedure 2. Reference value to define a hot spot Methods

17. 17 1. Traffic collision data Methods

18. SWITRS to TASAS 1. Flow of traffic collision data

19. 19 To detect HCCL, you need the following information: 1. Traffic collision data 3. Network screening procedure 2. Reference value to define a hot spot Methods

20. 20 2. Reference value to define a hot spot 2.1 Need Safety Performance Function (SPF). Methods: Safety Performance Function (SPF) SPF is mathematical relationship observed between explanatory variable and collision rate

21. 21 Source: Development of Safety Performance Functions for Two-Lane Roads Maintained by the Virginia Department of Transportation (June, 2010) Methods: Safety Performance Function (SPF)

22. 22 Source: Development of Safety Performance Functions for Two-Lane Roads Maintained by the Virginia Department of Transportation (June, 2010) Even if you have the same functional form, the value of the estimated parameters can vary. This affects the reference value for defining a hot spot. Methods: Safety Performance Function (SPF)

23. 23 2.2 Performance Measure 2. Reference value to define a hot spot 2.1 Need Safety Performance Function (SPF). SPF is mathematical relationship observed between explanatory variable and collision rate Methods: Safety Performance Function (SPF)

24. 24 AADT Collision Frequency A B Methods: Safety Performance Function (SPF)

25. 25 AADT Collision Frequency A B Critical Rate Methods: Safety Performance Function (SPF)

26. 26 AADT Collision Frequency A B 5 6 Methods: Safety Performance Function (SPF)

27. 27 Source: Highway Safety Manual Methods: Safety Performance Function (SPF)

28. 28 Source: Highway Safety Manual Average Crash Frequency Critical Rate Potential for Safety Improvement (PSI) Safety Performance Function (SPF)

29. 29 AADT Collision Frequency Observed Expected PSI Methods: Safety Performance Function (SPF)

30. 30 To detect a hot spots, you need the following: 1. Traffic collision data 3. Network screening procedure 2. Reference value to define a hot spot - SPF for each highway group and PSI Methods

31. 31 Highway Group Methods: Network Screening Procedure Segmentsite

32. 32 Highway GroupSegmentsite (i.e., two lane urban Freeway or six lane urban freeway) Methods: Network Screening Procedure

33. Two different sets of SPFs were used in present study: existing Caltrans SPF(SPF c ) and new SPF developed based on information presented in HSM (SPF o ). Caltrans Roadway Classification Description Relationship to New Roadway Classification H55Rural Freeway 5-6 lanesRSIF H56Rural Freeway 7 lanes or moreRSIF H61Suburban Freeway 5-6 lanesUSIF H62Suburban Freeway 7 lanes or moreUEIF H64Urban Freeway 5-6 lanesUSIF H65Urban Freeway 7-8 lanesUEIF H66Urban Freeway 9-10 lanesUEIF H67Urban Freeway 11 lanes or moreUEIF 0 100 200 300 H65H64H66H62H61H56H55H67 Highway Rate Group 0 100 200 300 400 500 UEIFUSIFRSIF Highway Group New Roadway Classification Description Relationship to Caltrans Roadway Classification RSIFRural Freeway 5 lanes or moreH55,H56 USIFUrban or Suburban Freeway 5-6 lanesH61,H64 UEIFUrban or Suburban Freeway 7 lanes or moreH62,H65,H66,H67 M ile Distribution of Caltrans roadway groups found in the study site Distribution of roadways in the study site after reclassification  This reclassification resulted in combining two or more groups into one group M ile Methods: Network Screening Procedure

34. 34 Log likelihood Ratio test indicated that SPF o fits the data better than SPF c. Methods: Network Screening Procedure

35. 35 Highway GroupSegmentsite - A portion of a facility that has a consistent roadway features and is defined by two endpoints. - See HSM Chapter 4, page 4-5 in volume 1. Methods: Network Screening Procedure

36. 36 Long Segment (0.05 ~ 11.38 miles) Roadway Group Seg1Seg2 Seg3 USIF UEIF USIF AADT USIFUEIFUSIF 88000 97000 Seg1Seg2Seg3Seg4Seg5Seg6 Roadway Group Short Segment (0.04 ~ 3.64 miles) Methods: Network Screening Procedure

37. 37 Long Segment (0.05 ~ 11.38 miles) Roadway Group Seg1Seg2 Seg3 USIF UEIF USIF AADT USIFUEIFUSIF 88000 97000 Seg1Seg2Seg3Seg4Seg5Seg6 Roadway Group Short Segment (0.04 ~ 3.64 miles) LS SS Methods: Network Screening Procedure

38. 38 Highway GroupSegmentsite section of roadway detected by hot spot identification procedure. Methods: Network Screening Procedure

39. 39 Highway GroupSegmentsite Can these change over the years? Methods: Network Screening Procedure

40. 40 Highway GroupSegmentsite Do all states have the same way of defining them? Methods: Network Screening Procedure

41. 41 Network screening procedure - Sliding Moving Window (SMW) - Peak Searching (PS) - Continuous Risk Profile (CRP) Methods: Network Screening Procedure

42. 42 l w L Traffic Flow SMW: Window(fixed size) is offset by a small increment(l) until the window reaches the end of segment(L) PSIs from all the windows are compared and the maximum value is used to represent PSI for the whole segment Segments are ranked in order of their PSIs Sliding Moving Window (SMW) Methods: Network Screening Procedure

43. 43 Sliding Moving Window (SMW) Methods: Network Screening Procedure

44. 44 PS: Sizes of windows(w 1,w 2, …) are increased until variation of their PSIs passes a test of variation or the size of the window reaches the length of entire segment(L) Similar to SMW, maximum PSI of all the windows represents PSI for the whole segment and is used to rank the segment w1w1 L w1w1 w1w1 w1w1 w1w1 w2w2 w2w2 w2w2 w2w2 w N =L … Peak Searching (PS) Methods: Network Screening Procedure

45. 45 Peak Searching (PS) Methods: Network Screening Procedure

46. 46 Collision Frequency SPF CRP sisi eiei s i+1 e i+1 s i+2 e i+2 AADT 1 AADT 2 f1f1 f2f2 PostMile CRP: Filter out the random noise in the data using weighted moving average technique and plots the collision risk profile along the freeway s i and e i : Endpoints of site, “B”: SPF collision frequency, “A”: Excess collision frequency A B Continuous Risk Profile (CRP) Network Screening Procedure

47. 47 Continuous Risk Profile (CRP) Network Screening Procedure PSI of each site is calculated from observed collision frequency(A+B) using EB method to rank the sites

48. 48 The objective of the study was to compare the performance of SMW, PS and CRP using empirical data and to this end traffic collision data collected over 663 miles of freeways located in California has been evaluated. Network Screening Procedure

49. 49 SPF c SPF o LS SS SMW PS CRP SMW PS CRP LS SS SMW PS CRP SMW PS CRP Then, compared the result with THU (True Hot Spot) Findings

50. 50 Comparison of number of sites identified to detect all THS 0 6.5 0 THS M ile Site Rank 57 0 0 Site Rank THS M ile 6.5 57 113114 SPF C SMW PS CRP Short Segment (SS) Long Segment (LS) 6672 SPF C Short Segment (SS) Long Segment (LS) 0 0 Site Mile THS M ile 17% 11% 10% 6.5 376169 0 0 Site Mile THS M ile 17% 10% 6.5 3767

51. 51 Comparison of number of sites identified to detect all THS 0 0 6.5 546876 Site Rank THS M ile 0 0 6.5 54 THS M ile Site Rank 3031 SMW PS CRP Short Segment (SS) Long Segment (LS) SPF O Short Segment (SS) Long Segment (LS) SPF O 0 0 23% 11% 10% 6.5 285867 Site Mile THS M ile 0 0 23% 12% 10% 6.5 285666 Site Mile THS M ile

52. 52 Collision Frequency AADT 1 AADT 2 PostMile Continuous Risk Profile (CRP) Network Screening Procedure

53. 53 Findings 1. Traffic collision data 3. Network screening procedure 2. Reference value to define a hot spot

54. 54 Findings 1. Traffic collision data 3. Network screening procedure 2. Reference value to define a hot spot Missing traffic data

55. 55 Findings 1. Traffic collision data 3. Network screening procedure 2. Reference value to define a hot spot Missing traffic data SPF (Poisson or NB?) Did it include all the relevant parameters? How accurate is the AADT?

56. 56 Findings 1. Traffic collision data 3. Network screening procedure 2. Reference value to define a hot spot Missing traffic data SPF (Poisson or NB?) Did it include all the relevant parameters? How accurate is the AADT? Should Caltrans use one SPF for entire state? Do we need meta data, can we afford to build one?

57. Motivation and Background StateSize in mi 2 OH44825 NC53819 VA42775 Combined Size141419 StateSize in mi 2 MN86946 WA71362 Combined Size158308 StateSize in mi 2 CA163696

58. 58 Source: Development of Safety Performance Functions for Two-Lane Roads Maintained by the Virginia Department of Transportation (June, 2010) Methods: Safety Performance Function (SPF)

59. 59 Comparison of number of sites identified to detect all THS 0 6.5 0 THS M ile Site Rank 57 0 0 Site Rank THS M ile 6.5 57 113114 SPF C SMW PS CRP Short Segment (SS) Long Segment (LS) 6672 0 0 6.5 546876 Site Rank THS M ile 0 0 6.5 54 THS M ile Site Rank 3031 Short Segment (SS) Long Segment (LS) SPF O

60. 60 Comparison of number of sites identified to detect all THS SMW PS CRP Short Segment (SS) Long Segment (LS) SPF O 0 0 23% 11% 10% 6.5 285867 Site Mile THS M ile 0 0 23% 12% 10% 6.5 285666 Site Mile THS M ile SPF C Short Segment (SS) Long Segment (LS) 0 0 Site Mile THS M ile 17% 11% 10% 6.5 376169 0 0 Site Mile THS M ile 17% 10% 6.5 3767

61. 61 Findings 1. Traffic collision data 3. Network screening procedure 2. Reference value to define a hot spot Missing traffic data SPF (Poisson or NB?) Did it include all the relevant parameters? How accurate is the AADT? Should Caltrans use one SPF for entire state? Do we need meta data, can we afford to build one? SMW? PS? CRP?

62. 62 Concluding remarks Using SPFs that better fits the traffic collision data improved the performance of all three methods: Using different SPF markedly changed the number of sites required by SMW and PS to cover THS, but did not significantly alter that of CRP method.

63. 63 Different segment definitions can significantly change the length of the site detected in SMW and PS methods while the length of the site is independent of segment length in CRP method. Decreasing the length of the segment from LS to SS resulted in marked increase in the number of sites that needs to be investigated to cover all the sites in THS using SMW and PS methods while that of CRP did not change Concluding remarks

64. 64 Using different SPF markedly changed the number of sites required by SMW and PS to cover THS, but did not significantly alter that of CRP method. CRP method has potential for reducing the amount of time that a safety engineer needs to spend for sites investigation and exposed to live traffic Concluding remarks Data requirement for CRP is the same as PS and SMW.

65. 65 Q & A koohong@gmail.com Chung, K., Jang, K., Madanat, S. and Washington, S. Proactive Detection of High Collision Concentration Locations on Highways (2011). Transportation Research Part A: Policy and 21 Practice. Chung, K., Ragland D., Samer, M. and Oh, S. (2009) The Continuous Risk Profile Approach for the Identification of High Collision Concentration Locations on Highways, Transportation and Traffic Theory, Springer, New York. pp 463-480. Kwon, O., Park, M, Chung, K., and Yeo, H. (2012) Comparing the Performance of Sliding Moving Window, Peak Searching, and Continuous Risk Profile Methods for Identifying High Collision Concentration Locations, accepted for publication at Accident Analysis and Prevention.