ABSTRACT
Telematics1 techniques enable insurers to capture the driving behavior of policyholders and correspondingly offer the personalized vehicle insurance rate, namely the usage-based insurance (UBI). A risky driving behavior scoring model for the personalized automobile insurance pricing was proposed based on telematics data. Firstly, three typical UBI pricing modes were analyzed. Drive behavior rate factors (DBRF) pricing mode was proposed based on mileage rate factors (MRF), in which insurance rate for each vehicle can be determined by the evaluation of individual driving behavior using OBD data. Then, on the basis of the analysis of influencing factors of safe driving, a driving behavior score model was established for DBRF by the improved EW-AHP (Entropy Weight- Analytic Hierarchy Process) Method. Finally, driving behavior scores of 100 drivers were computed by using the data collected from a 6-month field experiment. The results of three statistics analysis showed that the driving behavior score model could effectively reflect the risk level of driver's safe driving and provide a basis for the individual discount or surcharge that insurers offer to their policyholders.
- Yanagihara M, Uno N, Nakamura T. Latent class analysis for driving behavior on merging section{J}. Transportation Research Procedia, 2015,(6):259--271.Google ScholarCross Ref
- Xingping Yan, Hui Zhang, Chaozhong Wu, et al. Research progress and prospect of road traffic driving behavior {J}. Journal of Transportation Information and Safety, 2013:31(01):45--51.Google Scholar
- Campbell K. Detailed planning for research on making a significant improvement in highway safety: study 2-safety{R}. F-SHRP Web Document 2(NCHRP Project 20-58{2}): Contractor's Final Report, Transportation Research Board of The National Academics, 2003.Google Scholar
- Litman T. Pay-As-You-Drive insurance: recommendations for implementation{R}. Victoria Transport Policy Institute, 2011.Google Scholar
- Wu K.F, Aguero-Valverde J, Jovanis P.P. Using naturalistic driving data to explore the association between traffic safety-related events and crash risk at driver level {J}. Accident Analysis & Prevention, 2014, 72:210--218.Google ScholarCross Ref
- Lotan T, Toledo T. In-Vehicle data recorder for evaluation of driving behavior and safety{J}. Journal of Transportation Research Board, 2006, (1):112--119.Google Scholar
- Yulong Pei, Xupeng Zhang. Analysis on bad driving behavior characteristics {J}. Journal of Transportation Information and Safety, 2009, 27(03):81--84.Google Scholar
- Mingke Zhang, Haifeng Bai, Xiaofei Xie. Research on driving behavior risk and related factors of driver{J}. Peking University Studies(Natural Science Edition), 2008,44(03):475--482.Google Scholar
- De Winter J.C.F, Dodou D. The driver behavior questionnaire as a predictor of accidents: a meta-analysis {J}. Journal of Safety Research, 2010, 41(6):463--70.Google ScholarCross Ref
- Haiqin Li, Li Li. Analysis of unsafe driving behavior based on questionnaire {J}. Automobile Applied Technology, 2015, (02):145--148.Google Scholar
- Dozza M, Gonzalez N.P. Recognizing safety-critical events from naturalistic driving data {J}. Procedia -- Social and Behavioral Sciences, 2012, 48(2307):505--515.Google Scholar
- LITMANT. Distance-based vehicle insurance feasibility, benefits and cost: comprehensive technical report{R}. Viloria Transport Policy Institule, 2011.Google Scholar
- Progressive Insurance. Linking driving behavior to automobile accidents and insurance rates: an analysis of five billion miles driven{R}.USA, 2014.7.Google Scholar
- Maclean A.W, Davies D.R.T, Thiele K. The hazards and prevention of driving while be sleepy {J}. Sleep Medicine Reviews, 2003, 7(6):507--521.Google ScholarCross Ref
- Martain J.L. Relationship between crash rate and hourly traffic flow on Interurban motorway{J}. Accident Analysis and Prevention, 2002, 34(5):619--629.Google ScholarCross Ref
- Traffic Management Bureau of the Public Security Ministry. Annual report on road traffic accident in China in 2014{R}.2014.Google Scholar
- Ferreira J., Minike E. Measuring Per Mile Risk for Pay-As-You-Drive Automobile. Transportation Research Record Journal of the Transportation Research Board, 2012, 24 (2297):97--103Google ScholarCross Ref
- Boucher J. Pay-as-you-drive Insurance: The Effect of the Kilometers on the Risk of Accident, Anales Del Instituto De Actuarios Españoles,2013, 19:135--154.Google Scholar
- Staplin L, Gish K.W, Joyce J. 'Low mileage bias' and related policy implications -- a cautionary note. Accident Analysis & Prevention, 2008,40 (3), 1249--1252.Google ScholarCross Ref
- Langford J, Koppel S, McCarthy D, Srinivasan S. In defence of the 'low-mileage bias'. Accident Analysis & Prevention, 2008,40 (6), 1996--1999.Google ScholarCross Ref
- Paefgen J, Staake T, Fleisch E. Multivariate exposure modeling of accident risk: Insights from Pay-as-you-drive insurance data. Transportation Research Part A: Policy and Practice, 2014,61:27--40.Google ScholarCross Ref
- Davis, Gary A; Davuluri, Sujay; Pei, Jian Ping. A Case Control Study of Speed and Crash Risk, 2006.Google Scholar
- Elvik, R., Christensen, P. y Amundsen, A. (2004) Speed and road accidents. An evaluation of the Power Model. TØI report 740/2004. Institute of Transport Economics TOI, Oslo.Google Scholar
- Jun, J, Ogle, J. y Guensler, R. (2007) Relationships between Crash Involvement and Temporal-Spatial Driving Behavior Activity Patterns: Use of Data for Vehicles with Global Positioning Systems. Transportation Research Record, 2019, 246--255.Google ScholarCross Ref
- Klauer, S. G, T. A. Dingus, V. L. Neale, J. D. Sudweeks, and D. J. Ramsey, 2009. Comparing Real-world Behaviors of Drivers with High versus Low Rates of Crashes and Near-crashes, National Highway Traffic Safety Administration, Report No. DOT HS 811 091.Google Scholar
- Russell Henk, P.E, Val Pezoldt, Bernie Fette, Shedding light on the nighttime driving risk. An analysis of fatal crashes under dark conditions in the U.S., 1999--2008. Texas Transportation Institution, 2010.Google Scholar
- Klauer S.G. Risky driving report{R}. Virginia Polytechnic Institute and State University, 2006.Google Scholar
- Lianzeng Zhang, Baige Duan. Research on effect of mileage on the net insurance premium {J}. Insurance Studies, 2012, (6):29--38.Google Scholar
- No O, Carrol A, Multer J, et al. Research and Special Programs administration. Commerial Transportation Operator Fatigue Management Reference{R}. U.S Department of Transportation 2003.7.Google Scholar
- Stuttsa J. C, Wilkinsb J. W, Osbergc J. S, Vaughnb B. V.. Driver risk factors for sleep-related crashes. Accident Analysis and Prevention 35 (2003) 321--331.Google ScholarCross Ref
- Shuzhan Hou, Xiaorui Sun, Yyulong He, et al. Study on the relationship between traffic accident severity and traffic flow characteristics of Freeway{J}. China Safety Science Journal, 2011, 21(09):106--112.Google Scholar
- Matthew T.F, Climatology D.F. Chicago O'hare international airport July 1996-April 2002{J}. Bulletin of the American Meteorological Society, 2004, 85(4): 515--517.Google Scholar
- Nilsson, G. (2004). Traffic safety dimensions and the Power Model to describe the effect of speed on safety. Bulletin 221. Lund Institute of Technology, Department of Technology and Society, Traffic Enginering, Lund.Google Scholar
- Jinwei Guo, Xuqiang Fu, Xiang Gao, et al. An improved weight calculation method for multi objective decision making {J}. Journal of Xidian Univerisity(Natural Science Edition), 2014, 41(6):118--125.Google Scholar
Index Terms
- A Risky Driving Behavior Scoring Model for the Personalized Automobile Insurance Pricing
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