Which set of factors contribute to increase the likelihood of pedestrian fatality in road crashes?

References

• Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. Paper presented at the Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, Santiago de Chile, Chile.
   Google Scholar
• Alikhani, M., Nedaie, A., & Ahmadvand, A. (2013). Presentation of clustering-classification heuristic method for improvement accuracy in classification of severity of road accidents in Iran. Safety Science, 60, 142–150.
   Web of Science ®Google Scholar
• Aziz, H., Ukkusuri, S.V., & Hasan, S. (2013). Exploring the determinants of pedestrian–vehicle crash severity in New York City. Accident Analysis and Prevention, 50, 1298–1309.
   PubMed Web of Science ®Google Scholar
• Borzendowski, S.A. W., Rosenberg, R.L., Sewall, A.S., & Tyrrell, R.A. (2013). Pedestrians' estimates of their own nighttime conspicuity are unaffected by severe reductions in headlight illumination. Journal of safety research, 47, 25–30.
   PubMed Web of Science ®Google Scholar
• Chang, L.-Y., & Chen, W.-C. (2005). Data mining of tree-based models to analyze freeway accident frequency. Journal of Safety Research, 36(4), 365–375.
   PubMed Web of Science ®Google Scholar
• Chen, W.-H., & Jovanis, P. (2000). Method for identifying factors contributing to driver-injury severity in traffic crashes. Transportation Research Record: Journal of the Transportation Research Board, 1717, 1–9.
   Google Scholar
• Dai, D. (2012). Identifying clusters and risk factors of injuries in pedestrian–vehicle crashes in a GIS environment. Journal of Transport Geography, 24, 206–214.
   Web of Science ®Google Scholar
• de Oña, J., López, G., Mujalli, R., & Calvo, F. J. (2013). Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks. Accident Analysis and Prevention, 51, 1–10.
   PubMed Web of Science ®Google Scholar
• Delen, D., Sharda, R., & Bessonov, M. (2006). Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks. Accident Analysis and Prevention, 38(3), 434–444.
   PubMed Web of Science ®Google Scholar
• Edirisinghe, A.S., Kitulwatte, I.D., & Senarathne, U.D. (2015). A study into blood alcohol concentration in fatal accidents among vulnerable road users in a tertiary care hospital Sri Lanka. International Journal of Injury Control and Safety Promotion, 22(2), 158–164.
   PubMed Web of Science ®Google Scholar
• Geurts, K., Wets, G., Brijs, T., & Vanhoof, K. (2003). Profiling of high-frequency accident locations by use of association rules. Transportation Research Record: Journal of the Transportation Research Board, 1840, 123–130.
   Google Scholar
• Kaimila, B., Yamashina, H., Arai, A., & Tamashiro, H. (2013). Road traffic crashes and fatalities in Japan 2000–2010 with special reference to the elderly road user. Traffic Injury Prevention, 14(8), 777–781.
   PubMed Web of Science ®Google Scholar
• Kantardzic, M. (2011). Data mining: Concepts, models, methods, and algorithms. Hoboken, NJ: John Wiley & Sons.
   Google Scholar
• Kaplan, S., & Prato, C.G. (2012). Risk factors associated with bus accident severity in the United States: A generalized ordered logit model. Journal of Safety Research, 43(3), 171–180.
   PubMed Web of Science ®Google Scholar
• Kim, J.-K., Ulfarsson, G. F., Shankar, V.N., & Mannering, F.L. (2010). A note on modeling pedestrian-injury severity in motor-vehicle crashes with the mixed logit model. Accident Analysis and Prevention, 42(6), 1751–1758.
   PubMed Web of Science ®Google Scholar
• Kumar, S., & Toshniwal, D. (2016). A data mining approach to characterize road accident locations. Journal of Modern Transportation, 24(1), 62–72.
   Web of Science ®Google Scholar
• Lefler, D.E., & Gabler, H.C. (2004). The fatality and injury risk of light truck impacts with pedestrians in the United States. Accident Analysis and Prevention, 36(2), 295–304.
   PubMed Web of Science ®Google Scholar
• Lord, D., & Mannering, F. (2010). The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives. Transportation Research Part A: Policy and Practice, 44(5), 291–305.
   Web of Science ®Google Scholar
• Mohamed, M. G., Saunier, N., Miranda-Moreno, L.F., & Ukkusuri, S.V. (2013). A clustering regression approach: A comprehensive injury severity analysis of pedestrian–vehicle crashes in New York, US and Montreal, Canada. Safety Science, 54, 27–37.
   Web of Science ®Google Scholar
• Montella, A. (2011). Identifying crash contributory factors at urban roundabouts and using association rules to explore their relationships to different crash types. Accident Analysis and Prevention, 43(4), 1451–1463.
   PubMed Web of Science ®Google Scholar
• Moudon, A.V., Lin, L., Jiao, J., Hurvitz, P., & Reeves, P. (2011). The risk of pedestrian injury and fatality in collisions with motor vehicles, a social ecological study of state routes and city streets in King County, Washington. Accident Analysis and Prevention, 43(1), 11–24.
   PubMed Web of Science ®Google Scholar
• Mujalli, R.O., & de Ona, J. (2011). A method for simplifying the analysis of traffic accidents injury severity on two-lane highways using Bayesian networks. Journal of Safety Research, 42(5), 317–326.
   PubMed Web of Science ®Google Scholar
• Nie, J., Li, G., & Yang, J. (2015). A study of fatality risk and head dynamic response of cyclist and pedestrian based on passenger car accident data analysis and simulations. Traffic Injury Prevention, 16(1), 76–83.
   PubMed Web of Science ®Google Scholar
• Nisbet, R., Elder, IV, J., & Miner, G. (2009). Handbook of statistical analysis and data mining applications. Cambridge, MA: Academic Press.
   Google Scholar
• Pande, A., & Abdel-Aty, M. (2009). Market basket analysis of crash data from large jurisdictions and its potential as a decision support tool. Safety Science, 47(1), 145–154.
   Web of Science ®Google Scholar
• Pour-Rouholamin, M., & Zhou, H. (2016). Investigating the risk factors associated with pedestrian injury severity in Illinois. Journal of safety research, 57, 9–17.
   PubMed Web of Science ®Google Scholar
• Savolainen, P.T., Mannering, F.L., Lord, D., & Quddus, M.A. (2011). The statistical analysis of highway crash-injury severities: A review and assessment of methodological alternatives. Accident Analysis and Prevention, 43(5), 1666–1676.
   PubMed Web of Science ®Google Scholar
• Tavakoli Kashani, A., & Besharati, M.M. (2017). Fatality rate of pedestrians and fatal crash involvement rate of drivers in pedestrian crashes: A case study of Iran. International Journal of Injury Control and Safety Promotion, 24(2), 222–231.
   PubMed Web of Science ®Google Scholar
• Tavakoli Kashani, A., Rabieyan, R., & Besharati, M.M. (2014). A data mining approach to investigate the factors influencing the crash severity of motorcycle pillion passengers. Journal of Safety Research, 51, 93–98.
   PubMed Web of Science ®Google Scholar
• Weng, J., Zhu, J.-Z., Yan, X., & Liu, Z. (2016). Investigation of work zone crash casualty patterns using association rules. Accident Analysis and Prevention, 92, 43–52.
   PubMed Web of Science ®Google Scholar
• Zajac, S.S., & Ivan, J.N. (2003). Factors influencing injury severity of motor vehicle–crossing pedestrian crashes in rural Connecticut. Accident Analysis and Prevention, 35(3), 369–379.
   PubMed Web of Science ®Google Scholar
• Zhang, W., Gkritza, K., Keren, N., & Nambisan, S. (2011). Age and gender differences in conviction and crash occurrence subsequent to being directed to Iowa's driver improvement program. Journal of Safety Research, 42(5), 359–365.
   PubMed Web of Science ®Google Scholar