Journal of Urban Health

, Volume 94, Issue 6, pp 855–868 | Cite as

Neighborhood Influences on Vehicle-Pedestrian Crash Severity

  • Alireza Toran Pour
  • Sara Moridpour
  • Richard Tay
  • Abbas Rajabifard
Article
  • 91 Downloads

Abstract

Socioeconomic factors are known to be contributing factors for vehicle-pedestrian crashes. Although several studies have examined the socioeconomic factors related to the location of the crashes, limited studies have considered the socioeconomic factors of the neighborhood where the road users live in vehicle-pedestrian crash modelling. This research aims to identify the socioeconomic factors related to both the neighborhoods where the road users live and where crashes occur that have an influence on vehicle-pedestrian crash severity. Data on vehicle-pedestrian crashes that occurred at mid-blocks in Melbourne, Australia, was analyzed. Neighborhood factors associated with road users’ residents and location of crash were investigated using boosted regression tree (BRT). Furthermore, partial dependence plots were applied to illustrate the interactions between these factors. We found that socioeconomic factors accounted for 60% of the 20 top contributing factors to vehicle-pedestrian crashes. This research reveals that socioeconomic factors of the neighborhoods where the road users live and where the crashes occur are important in determining the severity of the crashes, with the former having a greater influence. Hence, road safety countermeasures, especially those focussing on the road users, should be targeted at these high-risk neighborhoods.

Keywords

Vehicle-pedestrian crashes Boosted regression tree Neighborhood socioeconomic influences Neighborhoods where road users live Neighborhood where crash occur 

Notes

Acknowledgements

We acknowledge that this research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

References

  1. 1.
    Pucher J, Dijkstra L. Promoting safe walking and cycling to improve public health: lessons from The Netherlands and Germany. Am J Public Health. 2003;93(9):1509–16.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    WHO Global status report on road safety 2015. Geneva , Switzerland: World Health Organization, Department of Violence & Injury Prevention & Disability (VIP). 2015Google Scholar
  3. 3.
    Rifaat S, Tay R, Perez A, Barros AD. Effects of neighborhood street patterns on traffic collision frequency. J Transp Safety & Security. 2009;1(4):241–53.CrossRefGoogle Scholar
  4. 4.
    Rifaat SM, Tay R, de Barros A. Urban street pattern and pedestrian traffic safety. J Urban Design. 2012;17(3):337–52.CrossRefGoogle Scholar
  5. 5.
    Li D, Ranjitkar P, Zhao Y, Yi H, Rashidi S. Analyzing pedestrian crash injury severity under different weather conditions. Traffic Inj Prev. 2016:00–0.Google Scholar
  6. 6.
    Tay R, Choi J, Kattan L, Khan A. A multinomial logit model of pedestrian–vehicle crash severity. Int J Sustain Transp. 2011;5(4):233–49.CrossRefGoogle Scholar
  7. 7.
    NHTSA NCfSaA. Pedestrians: 2014 data. Washington, DC: National Highway Traffic Safety Administration; 2016. DOT HS 812 270.Google Scholar
  8. 8.
    Mohamed MG, Saunier N, Miranda-Moreno LF, Ukkusuri SV. A clustering regression approach: a comprehensive injury severity analysis of pedestrian–vehicle crashes in New York, US and Montreal. Canada Saf Sci. 2013;54:27–37.CrossRefGoogle Scholar
  9. 9.
    Eluru N, Bhat CR, Hensher DA. A mixed generalized ordered response model for examining pedestrian and bicyclist injury severity level in traffic crashes. Accid Anal Prev. 2008;40(3):1033–54.CrossRefPubMedGoogle Scholar
  10. 10.
    Kim J-K, Ulfarsson GF, Shankar VN, Mannering FL. A note on modeling pedestrian-injury severity in motor-vehicle crashes with the mixed logit model. Accid Anal Prev. 2010;42(6):1751–8.CrossRefPubMedGoogle Scholar
  11. 11.
    Ukkusuri S, Miranda-Moreno LF, Ramadurai G, Isa-Tavarez J. The role of built environment on pedestrian crash frequency. Saf Sci. 2012;50(4):1141–51.CrossRefGoogle Scholar
  12. 12.
    Jones AP, Haynes R, Harvey IM, Jewell T. Road traffic crashes and the protective effect of road curvature over small areas. Health & Place. 2012;18(2):315–20.CrossRefGoogle Scholar
  13. 13.
    Ballesteros MF, Dischinger PC, Langenberg P. Pedestrian injuries and vehicle type in Maryland, 1995–1999. Accid Anal Prev. 2004;36(1):73–81.CrossRefPubMedGoogle Scholar
  14. 14.
    Cho G, Rodríguez DA, Khattak AJ. The role of the built environment in explaining relationships between perceived and actual pedestrian and bicyclist safety. Accid Anal Prev. 2009;41(4):692–702.CrossRefPubMedGoogle Scholar
  15. 15.
    Clifton KJ, Kreamer-Fults K. An examination of the environmental attributes associated with pedestrian–vehicular crashes near public schools. Accid Anal Prev. 2007;39(4):708–15.CrossRefPubMedGoogle Scholar
  16. 16.
    Gårder PE. The impact of speed and other variables on pedestrian safety in Maine. Accid Anal Prev. 2004;36(4):533–42.CrossRefPubMedGoogle Scholar
  17. 17.
    Miranda-Moreno LF, Morency P, El-Geneidy AM. The link between built environment, pedestrian activity and pedestrian–vehicle collision occurrence at signalized intersections. Accid Anal Prev. 2011;43(5):1624–34.CrossRefPubMedGoogle Scholar
  18. 18.
    Campos-Outcalt D, Bay C, Dellapenna A, Cota MK. Pedestrian fatalities by race/ethnicity in Arizona, 1990–1996. Am J Prev Med. 2002;23(2):129–35.CrossRefPubMedGoogle Scholar
  19. 19.
    Dougherty G, Pless IB, Wilkins R. Social class and the occurrence of traffic injuries and deaths in urban children. Can J Public Health. 1990;81(3):204–9.PubMedGoogle Scholar
  20. 20.
    Lyons RA, Towner E, Christie N, et al. The advocacy in action study a cluster randomized controlled trial to reduce pedestrian injuries in deprived communities. Inj Prev. 2008;14(2):e1.CrossRefPubMedGoogle Scholar
  21. 21.
    Cottrill CD, Thakuriah P. Evaluating pedestrian crashes in areas with high low-income or minority populations. Accid Anal Prev. 2010;42(6):1718–28.CrossRefPubMedGoogle Scholar
  22. 22.
    Borrell C, Plasència A, Huisman M, et al. Education level inequalities and transportation injury mortality in the middle aged and elderly in European settings. Inj Prev. 2005;11(3):138–42.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Amoh-Gyimah R, Sarvi M, Saberi M. Investigating the Effects of Traffic, Socioeconomic, and Land Use Characteristics on Pedestrian and Bicycle Crashes: A Case Study of Melbourne, Australia. Paper presented at: Transportation Research Board 95th Annual Meeting; 2016.Google Scholar
  24. 24.
    Toran Pour A, Moridpour S, Tay R, Rajabifard A. Modelling pedestrian crash severity at mid-blocks. Transportmetrica A: Transp Sci. 2017;13(3):273–97.CrossRefGoogle Scholar
  25. 25.
    Wier M, Weintraub J, Humphreys EH, Seto E, Bhatia R. An area-level model of vehicle-pedestrian injury collisions with implications for land use and transportation planning. Accid Anal Prev. 2009;41(1):137–45.CrossRefPubMedGoogle Scholar
  26. 26.
    Graham D, Glaister S, Anderson R. The effects of area deprivation on the incidence of child and adult pedestrian casualties in England. Accid Anal Prev. 2005;37(1):125–35.CrossRefPubMedGoogle Scholar
  27. 27.
    Wilde GJS. Social interaction patterns in driver behavior: an introductory review. Hum Factors. 1976;18(5):477–92.CrossRefGoogle Scholar
  28. 28.
    Ishaque MM, Noland RB. Behavioural issues in pedestrian speed choice and street crossing behaviour: a review. Transp Rev. 2008;28(1):61–85.CrossRefGoogle Scholar
  29. 29.
    Factor R, Mahalel D, Yair G. The social accident: a theoretical model and a research agenda for studying the influence of social and cultural characteristics on motor vehicle accidents. Accid Anal Prev. 2007;39(5):914–21.CrossRefPubMedGoogle Scholar
  30. 30.
    Agran PF, Winn DG, Anderson CL, Del Valle C. Family, social, and cultural factors in pedestrian injuries among Hispanic children. Inj Prev. 1998;4(3):188–93.CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Coughenour C, Clark S, Singh A, Claw E, Abelar J, Huebner J. Examining racial bias as a potential factor in pedestrian crashes. Accid Anal Prev. 2017;98:96–100.CrossRefPubMedGoogle Scholar
  32. 32.
    Australian Bureau of Statistics. Census Dictionary. In. Vol Cat no. 2901.0. Canberra: Australian Bureau of Statistics; 2001.Google Scholar
  33. 33.
    Chang L-Y, Wang H-W. Analysis of traffic injury severity: an application of non-parametric classification tree techniques. Accid Anal Prev. 2006;38(5):1019–27.CrossRefPubMedGoogle Scholar
  34. 34.
    Kashani AT, Mohaymany AS. Analysis of the traffic injury severity on two-lane, two-way rural roads based on classification tree models. Saf Sci. 2011;49(10):1314–20.CrossRefGoogle Scholar
  35. 35.
    Li X, Lord D, Zhang Y, Xie Y. Predicting motor vehicle crashes using support vector machine models. Accid Anal Prev. 2008;40(4):1611–8.CrossRefPubMedGoogle Scholar
  36. 36.
    Abellán J, López G, de Oña J. Analysis of traffic accident severity using decision rules via decision trees. Expert Syst Appl. 2013;40(15):6047–54.CrossRefGoogle Scholar
  37. 37.
    Chang L-Y, Chien J-T. Analysis of driver injury severity in truck-involved accidents using a non-parametric classification tree model. Saf Sci. 2013;51(1):17–22.CrossRefGoogle Scholar
  38. 38.
    Jung S, Qin X, Oh C. Improving strategic policies for pedestrian safety enhancement using classification tree modeling. Transp Res A Policy Pract. 2016;85:53–64.CrossRefGoogle Scholar
  39. 39.
    Lord D, van Schalkwyk I, Chrysler S, Staplin L. A strategy to reduce older driver injuries at intersections using more accommodating roundabout design practices. Accid Anal Prev. 2007;39(3):427–32.CrossRefPubMedGoogle Scholar
  40. 40.
    Pham M-H, Bhaskar A, Chung E, Dumont A-G. Random forest models for identifying motorway rear-end crash risks using disaggregate data. Paper presented at: Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference; 2010.Google Scholar
  41. 41.
    Jiang X, Abdel-Aty M, Hu J, Lee J. Investigating macro-level hotzone identification and variable importance using big data: a random forest models approach. Neurocomputing. 2016;181:53–63.CrossRefGoogle Scholar
  42. 42.
    Chung Y-S. Factor complexity of crash occurrence: an empirical demonstration using boosted regression trees. Accid Anal Prev. 2013;61:107–18.CrossRefPubMedGoogle Scholar
  43. 43.
    Lee C, Li X. Predicting driver injury severity in single-vehicle and two-vehicle crashes with boosted regression trees. Transp Res Record: J Transp Res Board. 2015;2514:138–48.CrossRefGoogle Scholar
  44. 44.
    Xu C, Liu P, Wang W, Li Z. Identification of freeway crash-prone traffic conditions for traffic flow at different levels of service. Transp Res A Policy Pract. 2014;69:58–70.CrossRefGoogle Scholar
  45. 45.
    Elith J, Leathwick JR, Hastie T. A working guide to boosted regression trees. J Anim Ecol. 2008;77(4):802–13.CrossRefPubMedGoogle Scholar
  46. 46.
    Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2001:1189–232.Google Scholar
  47. 47.
    Matignon R. Data Mining Using SAS Enterprise Miner. South Sanfrancisco, CA: John Wiley & Sons; 2007.Google Scholar
  48. 48.
    Breiman L, Friedman J, Stone CJ, Olshen RA. Classification and regression trees. New York: CRC press; 1984.Google Scholar
  49. 49.
    Ridgeway G. Generalized boosted models: a guide to the gbm package. Update. 2007;1(1):2007.Google Scholar
  50. 50.
    Team RDC. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2013. In: ISBN 3–900051–07-0; 2014.Google Scholar
  51. 51.
    Kuhn M. Caret package. J Stat Softw . 2008;28(5).Google Scholar
  52. 52.
    Verzosa N, Miles R. Severity of road crashes involving pedestrians in metro manila. Philippines Accid Anal Prev. 2016;94:216–26.CrossRefPubMedGoogle Scholar
  53. 53.
    Harwood DW, Bauer KM, Richard KR, et al. Pedestrian Safety Prediction Methodology. NCHRP Web-only Document 129: Phase III. Transportation Research Board, Washington, DC (2008). 2008.Google Scholar
  54. 54.
    Tulu GS, Washington S, Haque MM, King MJ. Investigation of pedestrian crashes on two-way two-lane rural roads in Ethiopia. Accid Anal Prev. 2015;78:118–26.CrossRefPubMedGoogle Scholar
  55. 55.
    Noland RB, Oh L. The effect of infrastructure and demographic change on traffic-related fatalities and crashes: a case study of Illinois county-level data. Accid Anal Prev. 2004;36(4):525–32.CrossRefPubMedGoogle Scholar
  56. 56.
    Rifaat S, Tay R, de Barros A. Effect of land use, road infrastructure, socioeconomic and demographic characteristics on public transit usage. Paper presented at: International Conference of the Hong Kong Society for Transportation Studies (HKSTS), 16th, 2011, Hong Kong; 2011.Google Scholar
  57. 57.
    Sze NN, Wong SC. Diagnostic analysis of the logistic model for pedestrian injury severity in traffic crashes. Accid Anal Prev. 2007;39(6):1267–78.CrossRefPubMedGoogle Scholar
  58. 58.
    Shinar D, Schechtman E, Compton R. Self-reports of safe driving behaviors in relationship to sex, age, education and income in the US adult driving population. Accid Anal Prev. 2001;33(1):111–6.CrossRefPubMedGoogle Scholar
  59. 59.
    Hassan HM, Shawky M, Kishta M, Garib AM, Al-Harthei HA. Investigation of drivers’ behavior towards speeds using crash data and self-reported questionnaire. Accid Anal Prev. 2017;98:348–58.CrossRefPubMedGoogle Scholar
  60. 60.
    LaScala EA, Gerber D, Gruenewald PJ. Demographic and environmental correlates of pedestrian injury collisions: a spatial analysis. Accid Anal Prev. 2000;32(5):651–8.CrossRefPubMedGoogle Scholar
  61. 61.
    Clifton KJ, Burnier CV, Akar G. Severity of injury resulting from pedestrian–vehicle crashes: what can we learn from examining the built environment? Transp Res Part D: Transp Environ. 2009;14(6):425–36.CrossRefGoogle Scholar

Copyright information

© The New York Academy of Medicine 2017

Authors and Affiliations

  1. 1.School of EngineeringRMIT UniversityMelbourneAustralia
  2. 2.School of Business IT and LogisticsRMIT UniversityMelbourneAustralia
  3. 3.Department of Infrastructure EngineeringUniversity of MelbourneMelbourneAustralia

Personalised recommendations