Usage-based vehicle insurance: Driving style factors of accident probability and severity

References

• Anderson, T. (2007). Comparison of spatial methods for measuring road accident ‘hotspots’: A case study of London. Journal of Maps, 3(1), 55–63. doi:10.1080/jom.2007.9710827
   Google Scholar
• Baecke, P., & Bocca, L. (2017). The value of vehicle telematics data in insurance risk selection processes. Decision Support Systems, 98, 69–79. doi:10.1016/j.dss.2017.04.009
   Web of Science ®Google Scholar
• Bagdadi, O., & Várhelyi, A. (2011). Jerky driving—An indicator of accident proneness? Accident Analysis & Prevention, 43(4), 1359–1363. doi:10.1016/j.aap.2011.02.009
   PubMed Web of Science ®Google Scholar
• Bian, Y., Yang, C., Zhao, J. L., & Liang, L. (2018). Good drivers pay less: A study of usage-based vehicle insurance models. Transportation Research Part A: policy and Practice, 107, 20–34.
   Web of Science ®Google Scholar
• Bíl, M., Andrášik, R., & Janoška, Z. (2013). Identification of hazardous road locations of traffic accidents by means of kernel density estimation and cluster significance evaluation. Accident; Analysis and Prevention, 55, 265–273. doi:10.1016/j.aap.2013.03.003
   PubMed Web of Science ®Google Scholar
• Durduran, S. S. (2010). A decision making system to automatic recognize of traffic accidents on the basis of a GIS platform. Expert Systems with Applications, 37(12), 7729–7736. doi:10.1016/j.eswa.2010.04.068
   Web of Science ®Google Scholar
• Elliott, M. A., Armitage, C. J., & Baughan, C. J. (2003). Drivers' compliance with speed limits: An application of the theory of planned behavior. Journal of Applied Psychology, 88(5), 964–972. doi:10.1037/0021-9010.88.5.964
   PubMed Web of Science ®Google Scholar
• Erdogan, S., Yilmaz, I., Baybura, T., & Gullu, M. (2008). Geographical information systems aided traffic accident analysis system case study: City of Afyonkarahisar. Accident; Analysis and Prevention, 40(1), 174–181. doi:10.1016/j.aap.2007.05.004
   PubMed Web of Science ®Google Scholar
• French, D. J., West, R. J., Elander, J., & Wilding, J. M. (1993). Decision-making style, driving style, and self-reported involvement in road traffic accidents. Ergonomics, 36(6), 627–644. doi:10.1080/00140139308967925
   PubMed Web of Science ®Google Scholar
• Hill, A., Horswill, M. S., Whiting, J., & Watson, M. O. (2019). Computer-based hazard perception test scores are associated with the frequency of heavy braking in everyday driving. Accident; Analysis and Prevention, 122, 207–214. doi:10.1016/j.aap.2018.08.030
   PubMed Web of Science ®Google Scholar
• Husnjak, S., Peraković, D., Forenbacher, I., & Mumdziev, M. (2015). Telematics system in usage based motor insurance. Procedia Engineering, 100, 816–825. doi:10.1016/j.proeng.2015.01.436
   Google Scholar
• Kashani, A. T., & Mohaymany, A. S. (2011). Analysis of the traffic injury severity on two-lane, two-way rural roads based on classification tree models. Safety Science, 49(10), 1314–1320. doi:10.1016/j.ssci.2011.04.019
   Web of Science ®Google Scholar
• Kashani, A. T., Shariat-Mohaymany, A., & Ranjbari, A. (2012). Analysis of factors associated with traffic injury severity on rural roads in Iran. Journal of Injury & Violence Research, 4(1), 36–41. doi:10.5249/jivr.v4i1.67
   PubMedGoogle Scholar
• Kosuge, R., Okamura, K., Kihira, M., Nakano, Y., & Fujita, G. (2017). Predictors of driving outcomes including both crash involvement and driving cessation in a prospective study of Japanese older drivers. Accident Analysis & Prevention, 106, 131–140. doi:10.1016/j.aap.2017.05.019
   PubMed Web of Science ®Google Scholar
• Mohaymany, A. S., Shahri, M., & Mirbagheri, B. (2013). GIS-based method for detecting high-crash-risk road segments using network kernel density estimation. Geo-spatial Information Science, 16(2), 113–119.
   Google Scholar
• Mousavi, S. M., Zhang, Z., Parr, S. A., Pande, A., & Wolshon, B. (2019). Identifying high crash risk highway segments using jerk-cluster analysis. In International Conference on Transportation and Development 2019: Smarter and Safer Mobility and Cities (pp. 112-123). Reston, VA: American Society of Civil Engineers.
   Google Scholar
• Naude, C., Serre, T., Dubois-Lounis, M., Fournier, J. Y., Lechner, D., Guilbot, M., & Ledoux, V. (2019). Acquisition and analysis of road incidents based on vehicle dynamics. Accident; Analysis and Prevention, 130, 117–124. doi:10.1016/j.aap.2017.02.021
   PubMed Web of Science ®Google Scholar
• Olszewski, P., Szagała, P., Wolański, M., & Zielińska, A. (2015). Pedestrian fatality risk in accidents at unsignalized zebra crosswalks in Poland. Accident; Analysis and Prevention, 84, 83–91. doi:10.1016/j.aap.2015.08.008
   PubMed Web of Science ®Google Scholar
• Park, H. C., Kim, D. K., Kho, S. Y., & Park, P. Y. (2017). Cross-classified multilevel models for severity of commercial motor vehicle crashes considering heterogeneity among companies and regions. Accident; Analysis and Prevention, 106, 305–314. doi:10.1016/j.aap.2017.06.009
   PubMed Web of Science ®Google Scholar
• PTOLEMUS Consulting Group. (2016). Usage-based insurance global study. 2016 ed. Brussels: Ptolemus.
   Google Scholar
• Quddus, M. A. (2008). Time series count data models: An empirical application to traffic accidents. Accident; Analysis and Prevention, 40(5), 1732–1741. doi:10.1016/j.aap.2008.06.011
   PubMed Web of Science ®Google Scholar
• Sagberg, F., Selpi, B., Piccinini, G. F., & Engström, J. (2015). A review of research on driving styles and road safety. Human Factors, 57(7), 1248–1275.
   PubMed Web of Science ®Google Scholar
• Taubman-Ben-Ari, O., Mikulincer, M., & Gillath, O. (2004). The multidimensional driving style inventory—Scale construct and validation. Accident; Analysis and Prevention, 36(3), 323–332. doi:10.1016/S0001-4575(03)00010-1
   PubMed Web of Science ®Google Scholar
• Tesema, T. B., Abraham, A., & Grosan, C. (2005). Rule mining and classification of road traffic accidents using adaptive regression trees. International Journal of Simulation, 6(10), 80–94.
   Google Scholar