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Alternative Method for Identifying Crash Hotspot Using Detailed Crash Information from First Information Report (FIR)

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Abstract

Identification and improvement of crash hotspots is an important step towards road safety improvement. The Hotspot Identification (HSID) methods aim to identify a list of hotspots with minimum false identifications. HSID methods available in literature identifies hotspots using crash history information, traffic information and road geometry information. The crash history-based methods use Crash Frequency (CF) and severity (Equivalent Property Damage Only—EPDO) information only. The crash time and weather information, which determine the visibility and pavement conditions, are not considered. Researchers claim that simultaneous use of multiple HSID methods yield minimum false identifications. This work aims to utilize weather and time information, by introducing a Cause Index (CI) and a Time Index (TI), respectively, along with conventional EPDO method to systematically identify a group of hotspots using a multi-dimensional K-means clustering algorithm. The causes assigned in the CI are skid, night, fog and faulty geometry. TI categorizes crashes into day-time and night-time crashes. Use of only one or simultaneous multiple HSID measures to designate hotspots is not sufficient as these are based on three-year average crash occurrences. The crash occurrence pattern needs to be studied. The monthly crash occurrence pattern, checked using Runs Test, can confirm the hotspot list. In the proposed method, number of candidate hotspots is not a function of practitioners’ discretion. The proposed method was tested with road crash data of Patna, Bihar, India and its performance was compared with CF and EPDO methods. It was observed that the proposed method yielded minimum false identifications.

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References

  1. Hauer E, Persaud BN (1984) Problem of identifying hazardous locations using accident data. Transp Res Rec 975:36–43

    Google Scholar 

  2. Hauer E, Allery KB, Kononov J, Griffith SM (2004) How best to rank sites with promise. Transp Res Rec 1897:48–54

    Article  Google Scholar 

  3. Cheng W, Wang X (2010) Evaluation of the joint use of accident count and accident reduction potential to identify hotspot. Presented at 89th Annual Meeting of the Transportation Research Board, Washington, DC

  4. Deacon JA, Zegeer V, Deen R (1975) Identification of hazardous rural highway locations. Transp Res Rec 543:16–33

    Google Scholar 

  5. Hauer E, Kononov J, Allery B, Griffith SM (2002) Screening the road network for sites with promise. Transp Res Rec 1784:27–32

    Article  Google Scholar 

  6. Oh J, Washington S, Lee D (2010) Property damage crash equivalency factors for solving the crash frequency-severity dilemma: case study on South Korean rural roads. Trans Res Rec 2148:83–92

  7. Sen AK, Tiwari G, Upadhay V (2010) Estimating marginal external costs of transport in Delhi. Transport Pllicy 17:27–37

    Article  Google Scholar 

  8. Laughland DC, Haefner LE, Hall JW, Clough DR (1975) Methods for evaluating highway safety improvements. NCHRP Report 162. Transportation Research Board, Washington, D.C.

  9. Yakar F (2015) Identification of accident prone road sections by using relative frequency method. Prome-Traffic-Traffico 27(6):539–547

    Google Scholar 

  10. Elvik, R. (2007). State-of-the-art approaches to road accident black spot management and safety analysis of road networks. Report 1 of work package 6 of RIPCORD-ISEREST

  11. Persaud B, Lyon C (2007) Empirical Bayes before-after safety studies: lessons learned from two decades of experience and future directions. Accid Anal Prev 39:546–555

    Article  Google Scholar 

  12. Elvik R (2008) The predictive validity of empirical Bayes estimates of road safety. Accid Anal Prev 6(40):1964–1969

    Article  Google Scholar 

  13. Persaud B, Lan B, Lyon C, Bhim R (2010) Comparison of empirical Bayes and full Bayes approaches for before–after road safety evaluations. Accid Anal Prev 42(1):38–43

    Article  Google Scholar 

  14. Wang C, Quddus MA, Ison SG (2011) Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model. Accid Anal Prev 43(6):1979–1990

    Article  Google Scholar 

  15. McGuigan DRD (1981) The use of relationships between road accidents and traffic flow in the black spot identification. Traffic Eng Control 22:448–453

    Google Scholar 

  16. Miranda-Monero LF, Labbe A, Fu L (2007) Bayesian multiple testing procedures for hotspot identification. Accid Anal Prev 39(6):1192–1201

    Article  Google Scholar 

  17. Hauer E, Harwood DM, Council FM, Griffith MS (2001) Estimating safety by Empirical Bayes method: a tutorial. Transp Res Rec 1784:126–131

    Article  Google Scholar 

  18. Lord D, Ye F (2014) Comparing three commonly used crash severity models on sample size requirements: multinomial logit, ordered probit and mixed logit models. Anal Methods Accid Res 1:72–85

    Google Scholar 

  19. Miranda-Moreno LF, Heydari S, Lord D, Fu L (2013) Bayesian road safety analysis: Incorporation of past evidence and effect of hyper-prior choice. J Saf Res 46:31–40

    Article  Google Scholar 

  20. Pawlovich MD, Li W, Carriquiry A, Welch T (2006) Iowa’s experience with “Road Diet” measures: impacts on crash frequencies and crash rates assessed following a bayesian approach. Transportation Research Record Journal of the Transportation Research Board 1953(1):163-171

    Article  Google Scholar 

  21. Aul N, Davis G (2006) Using a Propensity score matching method and a hybrid bayesian method to estimate crash modification factors of signal installation. J Transp Res Board 1950:17–23

    Article  Google Scholar 

  22. Tunaru R (2002) Hierarchical Bayesian models for multiple count data. Austrian J Stat 31:221–229

    Google Scholar 

  23. Lee AS, Lin W, Gill GS, Cheng W (2018) An enhanced empirical Bayesian method for identifying road hotspots and predicting number of crashes. J Transp Saf Secur 11(5):562–578

    Google Scholar 

  24. Washington S, Afghari AP, Haque MM (2018) Detecting high-risk accident locations. Transp Sustain 11:351–382

    Article  Google Scholar 

  25. Ghadi M, Torok A (2019) A comparative of black spot identification methods and road accident segmentation methods. Accid Anal Prev 128:1–7

    Article  Google Scholar 

  26. Maskooni KE, Haghighi F (2008) Evaluation and statistical validation of blackspot identification methods. Int J Transp Eng 6:1–15. https://doi.org/10.22119/ijte.2017.52977

    Google Scholar 

  27. Abebe TM, Belayneh ZM (2018) Identifying and ranking dangerous road segments a case of Hawassa-Shashemene-Bulbula two lane way rural highway Ethiopia. J Transp Technol 08(03):151–174

    Google Scholar 

  28. Guo X, Wu L, Zou Y, Fawcett L (2019) Comparative analysis of empirical Bayes and Bayesian hierarchical models in hotspots identification. Transp Res Rec 2673(7):111–121

    Article  Google Scholar 

  29. Abdel-Aty M, Pande A (2007) Crash data analysis: collective vs individual crash level approach. J Saf Res 38(5):581–587

    Article  Google Scholar 

  30. Montella A (2010) A comparative analysis of hotspot identification method. Accid Anal Prev 42(2):571–581

    Article  Google Scholar 

  31. Pirdavani A, Brijs T, Geert W (2010) A Multiple criteria decision making approach for prioritizing accident hotspots in developing countries in the absence of crash data. Transp Res Board 30(1):97–113

    Google Scholar 

  32. ChikkaKrishna KN, Parida M, Jain SS (2017) Identifying safety factor associated with crash frequency and severity on nonurban four lane highway stretch in India. Journal of Transportation Safety and Security 9(Sup1): 6-32

  33. Aghajani AM, Dezfoulian SR, Arjroody RA, Rezaei RA (2016) Apply GIS to Identify the Spatial and Temporal pattern of road accident using spatial statistics (case study: Ilam Province, Iran). Transp Res Procedia 25:2126–2138

    Article  Google Scholar 

  34. Li Y, Liang C (2018) The analysis of spatial pattern and hotspots of aviation accident and ranking the potential risk airports based on GIS platform, Journal of Advanced Transportation, 2: 1-12

  35. Plug C, Xia J, Caulfield C (2011) Spatial and temporal visualisation techniques for crash analysis. Accid Anal Prev 43:1937–1946

    Article  Google Scholar 

  36. Xie Z, Yan J (2013) Detecting traffic accident clusters with network kernel density estimation and local spatial statistics:an integrated approach. J Transp Geogr 31:64–71

    Article  Google Scholar 

  37. Cheng W, Jia X (2014) Exploring an alternative method of hazardous location identification: using accident count and accident reduction potential jointly. J Tansp Saf Secur 7(1):40–55

    Google Scholar 

  38. Cheng W, Washington SP (2008) New criteria for evaluating methods of identifying hot spots. Transp Res Rec 2083(1):76–85

    Article  Google Scholar 

  39. Khasnabis S, Mishra S, Safi C (2011) Identification of hazardous locations: An alternative approach. Conference on Infrastructure, Sustainable Transportation & Urban Planning,CiSTUP, Bangalore, January 2011.

  40. Kononov J (2002) Identifying locations with potential for accident reductions: use of direct diagnostics and pattern recognition methodologies. Transp Res Rec 1784:153–158

    Article  Google Scholar 

  41. Higle JL, Hecht MB (1989) A comparison of techniques for the identification of hazardous locations. Transp Res Rec 1238:10–19

    Google Scholar 

  42. Nicholson JA (1986) The randomness of accident counts. Accid Anal Prev 18:193–198

    Article  Google Scholar 

  43. Aczel AD, Sounderpandian J (2010) Complete business statistics, 6th edn. Tata McGraw Hill Publishing Company Ltd., New Delhi

    Google Scholar 

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Authors and Affiliations

  1. National Institute of Technology Patna, Patna, India

    Santanu Barman & Ranja Bandyopadhyaya

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  1. Santanu Barman
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  2. Ranja Bandyopadhyaya
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Correspondence to Ranja Bandyopadhyaya.

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Barman, S., Bandyopadhyaya, R. Alternative Method for Identifying Crash Hotspot Using Detailed Crash Information from First Information Report (FIR). Transp. in Dev. Econ. 7, 16 (2021). https://doi.org/10.1007/s40890-021-00124-5

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