Skip to main content

Advertisement

Log in

Neighborhood Influences on Vehicle-Pedestrian Crash Severity

  • Published:
Journal of Urban Health Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  2. WHO Global status report on road safety 2015. Geneva , Switzerland: World Health Organization, Department of Violence & Injury Prevention & Disability (VIP). 2015

  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.

    Article  Google Scholar 

  4. Rifaat SM, Tay R, de Barros A. Urban street pattern and pedestrian traffic safety. J Urban Design. 2012;17(3):337–52.

    Article  Google Scholar 

  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.

  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.

    Article  Google Scholar 

  7. NHTSA NCfSaA. Pedestrians: 2014 data. Washington, DC: National Highway Traffic Safety Administration; 2016. DOT HS 812 270.

  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.

    Article  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  13. Ballesteros MF, Dischinger PC, Langenberg P. Pedestrian injuries and vehicle type in Maryland, 1995–1999. Accid Anal Prev. 2004;36(1):73–81.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  16. Gårder PE. The impact of speed and other variables on pedestrian safety in Maine. Accid Anal Prev. 2004;36(4):533–42.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

  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.

    Article  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  27. Wilde GJS. Social interaction patterns in driver behavior: an introductory review. Hum Factors. 1976;18(5):477–92.

    Article  Google Scholar 

  28. Ishaque MM, Noland RB. Behavioural issues in pedestrian speed choice and street crossing behaviour: a review. Transp Rev. 2008;28(1):61–85.

    Article  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  32. Australian Bureau of Statistics. Census Dictionary. In. Vol Cat no. 2901.0. Canberra: Australian Bureau of Statistics; 2001.

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

  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.

    Article  Google Scholar 

  42. Chung Y-S. Factor complexity of crash occurrence: an empirical demonstration using boosted regression trees. Accid Anal Prev. 2013;61:107–18.

    Article  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  45. Elith J, Leathwick JR, Hastie T. A working guide to boosted regression trees. J Anim Ecol. 2008;77(4):802–13.

    Article  CAS  PubMed  Google Scholar 

  46. Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2001:1189–232.

  47. Matignon R. Data Mining Using SAS Enterprise Miner. South Sanfrancisco, CA: John Wiley & Sons; 2007.

  48. Breiman L, Friedman J, Stone CJ, Olshen RA. Classification and regression trees. New York: CRC press; 1984.

    Google Scholar 

  49. Ridgeway G. Generalized boosted models: a guide to the gbm package. Update. 2007;1(1):2007.

    Google Scholar 

  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.

  51. Kuhn M. Caret package. J Stat Softw . 2008;28(5).

  52. Verzosa N, Miles R. Severity of road crashes involving pedestrians in metro manila. Philippines Accid Anal Prev. 2016;94:216–26.

    Article  PubMed  Google Scholar 

  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.

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

Download references

Acknowledgements

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

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alireza Toran Pour.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Toran Pour, A., Moridpour, S., Tay, R. et al. Neighborhood Influences on Vehicle-Pedestrian Crash Severity. J Urban Health 94, 855–868 (2017). https://doi.org/10.1007/s11524-017-0200-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11524-017-0200-z

Keywords

Navigation