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Research Article

Cumulative effect of driver’s historical violations on traffic accidents: New evidences from China

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References

  • Abdel-Aty, M., Hassan, H., & Siddiqui, C. (2012). Young drivers safety study final report. Florida Department of Transportation District, 5.
  • Akhtar, S., & Ziyab, A. H. (2013). Impact of the penalty points system on severe road traffic injuries in Kuwait. Traffic Injury Prevention, 14(7), 743–748. https://doi.org/10.1080/15389588.2012.749466
  • Alkinani, M. H., Khan, W. Z., & Arshad, Q. (2020). Detecting human driver inattentive and aggressive driving behavior using deep learning: Recent advances, requirements and open challenges. IEEE Access, 8, 105008–105030. https://doi.org/10.1109/ACCESS.2020.2999829
  • Alver, Y., Demirel, M. C., & Mutlu, M. M. (2014). Interaction between socio-demographic characteristics: Traffic rule violations and traffic crash history for young drivers. Accident; Analysis and Prevention, 72, 95–104. https://doi.org/10.1016/j.aap.2014.06.015
  • Ashikuzzaman, M., Akram, W., Anik, M. M. I., Hasan, M., Ali, M. S., & Jabid, T. (2021). PSO-ANN in preventing traffic collisions: A comparative study. Indonesian Journal of Electrical Engineering and Computer Science, 24(3), 1796–1803. https://doi.org/10.11591/ijeecs.v24.i3.pp1796-1803
  • Assemi, B., & Hickman, M. (2018). Relationship between heavy vehicle periodic inspections, crash contributing factors and crash severity. Transportation Research Part A: Policy and Practice, 113, 441–459. https://doi.org/10.1016/j.tra.2018.04.018
  • Barraclough, P., af Wåhlberg, A., Freeman, J., Watson, B., & Watson, A. (2016). Predicting crashes using traffic offences. A meta-analysis that examines potential bias between self-report and archival data. PloS One, 11(4), e0153390. https://doi.org/10.1371/journal.pone.0153390
  • Castillo-Manzano, J. I., & Castro-Nuño, M. (2012). Driving licenses based on points systems: Efficient road safety strategy or latest fashion in global transport policy? A worldwide meta-analysis. Transport Policy, 21, 191–201. https://doi.org/10.1016/j.tranpol.2012.02.003
  • Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. AI Access Foundation, (1). https://doi.org/10.1613/jair.953
  • Chen, M., & Chen, M. (2020). Modeling road accident severity with comparisons of logistic regression, decision tree and random forest. Information, 11(5), 270. https://doi.org/10.3390/info11050270
  • Chen, S., Shao, H., & Ji, X. (2021). Insights into factors affecting traffic accident severity of novice and experienced drivers: A machine learning approach. International Journal of Environmental Research and Public Health, 18(23), 12725. https://doi.org/10.3390/ijerph182312725
  • Czako, Z., Sebestyen, G., & Hangan, A. (2021). AutomaticAI – A hybrid approach for automatic artificial intelligence algorithm selection and hyperparameter tuning. Expert Systems with Applications, 182, 115225. https://doi.org/10.1016/j.eswa.2021.115225
  • De Paola, M., Scoppa, V., & Falcone, M. (2013). The deterrent effects of the penalty points system for driving offences: A regression discontinuity approach. Empirical Economics, 45(2), 965–985. https://doi.org/10.1007/s00181-012-0642-9
  • Delen, D., Tomak, L., Topuz, K., & Eryarsoy, E. (2017). Investigating injury severity risk factors in automobile crashes with predictive analytics and sensitivity analysis methods. Journal of Transport & Health, 4, 118–131. https://doi.org/10.1016/j.jth.2017.01.009
  • Dong, H., Jia, N., Tian, J., & Ma, S. (2019). The effectiveness and influencing factors of a penalty point system in China from the perspective of risky driving behaviors. Accident; Analysis and Prevention, 131, 171–179. https://doi.org/10.1016/j.aap.2019.06.005
  • Dong, S., Khattak, A., Ullah, I., Zhou, J., & Hussain, A. (2022). Predicting and analyzing road traffic injury severity using boosting-based ensemble learning models with SHAPley Additive exPlanations. International Journal of Environmental Research and Public Health, 19(5), 2925. https://doi.org/10.3390/ijerph19052925
  • Evans, L. (2014). Traffic fatality reductions: United States compared with 25 other countries. American Journal of Public Health, 104(8), 1501–1507. https://doi.org/10.2105/ajph.2014.301922
  • Factor, R. (2018). An empirical analysis of the characteristics of drivers who are ticketed for traffic offences. Transportation Research Part F: Traffic Psychology and Behaviour, 53, 1–13. https://doi.org/10.1016/j.trf.2017.12.001
  • Goldenbeld, C., Reurings, M., Van Norden, Y., & Stipdonk, H. (2011). The relation between traffic offences and road crashes: Preliminary study making use of Central Fine Collection Agency (CJIB) data.
  • Gu, X., Li, T., Wang, Y., Zhang, L., Wang, Y., & Yao, J. (2018). Traffic fatalities prediction using support vector machine with hybrid particle swarm optimization. Journal of Algorithms & Computational Technology, 12(1), 20–29. https://doi.org/10.1177/1748301817729953
  • Hakkert, A. S., Gitelman, V., Cohen, A., Doveh, E., & Umansky, T. (2001). The evaluation of effects on driver behavior and accidents of concentrated general enforcement on interurban roads in Israel. Accident; Analysis and Prevention, 33(1), 43–63. https://doi.org/10.1016/S0001-4575(00)00014-2
  • Hatfield, J., & Fernandes, R. (2009). The role of risk-propensity in the risky driving of younger drivers. Accident; Analysis and Prevention, 41(1), 25–35. https://doi.org/10.1016/j.aap.2008.08.023
  • Hou, L., Pei, Y., & He, Q. (2023). A car following model in the context of heterogeneous traffic flow involving multilane following behavior. Physica A: Statistical Mechanics and Its Applications, 632, 129307. https://doi.org/10.1016/j.physa.2023.129307
  • Hui, X., Wu, J., & Jian, C. (2006). K-means clustering versus validation measures: A data distribution perspective. IEEE Xplore. https://doi.org/10.1109/TSMCB.2008.2004559
  • Jonah, B. A. (1997). Sensation seeking and risky driving: A review and synthesis of the literature. Accident; Analysis and Prevention, 29(5), 651–665. https://doi.org/10.1016/S0001-4575(97)00017-1
  • Joo, Y. J., Kho, S. Y., Kim, D. K., & Park, H. C. (2022). A data-driven Bayesian network for probabilistic crash risk assessment of individual driver with traffic violation and crash records. Accident; Analysis and Prevention, 176, 106790. https://doi.org/10.1016/j.aap.2022.106790
  • Karacasu, M., & Er, A. (2011). An Analysis on Distribution of Traffic Faults in Accidents, Based on Driver’s Age and Gender: Eskisehir Case. Procedia - Social and Behavioral Sciences, 20, 776–785. https://doi.org/10.1016/j.sbspro.2011.08.086
  • Keall, M. D., Frith, W. J., & Patterson, T. L. (2004). The influence of alcohol, age and number of passengers on the night-time risk of driver fatal injury in New Zealand. Accident; Analysis and Prevention, 36(1), 49–61. https://doi.org/10.1016/S0001-4575(02)00114-8
  • Kim, D. H., Ramjan, L. M., & Mak, K. K. (2016). Prediction of vehicle crashes by drivers’ characteristics and past traffic violations in Korea using a zero-inflated negative binomial model. Traffic Injury Prevention, 17(1), 86–90. https://doi.org/10.1080/15389588.2015.1033689
  • Kumar, S., Toshniwal, D., & Parida, M. (2017). A comparative analysis of heterogeneity in road accident data using data mining techniques. Evolving Systems, 8(2), 147–155. https://doi.org/10.1007/s12530-016-9165-5
  • Ling, C. X., Huang, J., & Zhang, H. (2003). AUC: A statistically consistent and more discriminating measure than accuracy. Paper presented at the Ijcai.
  • Lv, H., Li, H., Sze, N. N., & Ren, G. (2022). The impacts of non-motorized traffic enforcement cameras on red light violations of cyclists at signalized intersections. Journal of Safety Research, 83, 310–322. https://doi.org/10.1016/j.jsr.2022.09.005
  • Ma, Z., Mei, G., & Cuomo, S. (2021). An analytic framework using deep learning for prediction of traffic accident injury severity based on contributing factors. Accident; Analysis and Prevention, 160, 106322. https://doi.org/10.1016/j.aap.2021.106322
  • Manual, N. R. C. T. R. B. T. F. o D. o t H. S., & Manual, T. O. J. T. F. o t H. S. (2010). Highway safety manual (Vol. 1). AASHTO.
  • Mehmood, A. (2010). Evaluating impact of demerit points system on speeding behavior of drivers. European Transport Research Review, 2(1), 25–30.https://doi.org/10.1007/s12544-010-0027-0
  • Mohammadi, G. (2011). Prevalence of seat belt and mobile phone use and road accident injuries amongst college students in Kerman, Iran. Chinese Journal of Traumatology (English Edition), 14(3), 165–169. https://doi.org/10.3760/cma.j.issn.1008-1275.2011.03.008
  • Mook, Y. H., & Changwan, K. (2012). A prediction model on traffic accidents and violation of traffic acts by driver’s characteristics [운전성향을 이용한 교통사고유발 및 교통법규위반 예측요인 연구]. Journal of the Korean Data Analysis Society, 14(5), 2821–2827. <Go to ISI>://KJD:ART001708268.
  • Moosavi, S., Samavatian, M. H., Parthasarathy, S., & Ramnath, R. (2019). A countrywide traffic accident dataset. arXiv PreprintarXiv:1906.05409. https://doi.org/10.48550/arXiv.1906.05409
  • Nakahara, S., Ichikawa, M., & Kimura, A. (2011). Population strategies and high-risk-individual strategies for road safety in Japan. Health Policy (Amsterdam, Netherlands), 100(2–3), 247–255. https://doi.org/10.1016/j.healthpol.2010.11.003
  • Oguchi, T. (2016). Achieving safe road traffic—The experience in Japan. IATSS Research, 39(2), 110–116. https://doi.org/10.1016/j.iatssr.2016.01.003
  • Precht, L., Keinath, A., & Krems, J. F. (2017). Effects of driving anger on driver behavior–Results from naturalistic driving data. Transportation Research Part F: Traffic Psychology and Behaviour, 45, 75–92. https://doi.org/10.1016/j.trf.2016.10.019
  • Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: Unbiased boosting with categorical features. Advances in Neural Information Processing Systems, 31
  • Rao, C. R. (1987). A unified approach to estimation in linear models with fixed and mixed effects.
  • Retallack, A. E., & Ostendorf, B. (2020). Relationship between traffic volume and accident frequency at intersections. International Journal of Environmental Research and Public Health, 17(4), 1393. https://doi.org/10.3390/ijerph17041393
  • Rothengatter, T. (1997). Errors and violations as factors in accident causation. Traffic and Transport Psychology. Theory and Application.
  • Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65. https://doi.org/10.1016/0377-0427(87)90125-7
  • Sagberg, F., & Sundfør, H. B. (2019). Self-reported deterrence effects of the Norwegian driver’s licence penalty point system. Transportation Research Part F: Traffic Psychology and Behaviour, 62, 294–304. https://doi.org/10.1016/j.trf.2019.01.012
  • Shaaban, K. (2017). Assessment of drivers’ perceptions of various police enforcement strategies and associated penalties and rewards. Journal of Advanced Transportation, 2017, 1–14. https://doi.org/10.1155/2017/5169176
  • Shankar, V., Mannering, F., & Barfield, W. (1995). Effect of roadway geometrics and environmental factors on rural freeway accident frequencies. Accident; Analysis and Prevention, 27(3), 371–389. https://doi.org/10.1016/0001-4575(94)00078-z
  • Shawky, M., Al-Badi, Y., Sahnoon, I., & Al-Harthi, H. (2017). The relationship between traffic rule violations and accident involvement records of drivers. In Advances in human aspects of transportation (pp. 745–755) Springer.
  • Shinar, D. (2017). Traffic safety and human behavior. Emerald Group Publishing.
  • Smith, S. S., Horswill, M. S., Chambers, B., & Wetton, M. (2009). Hazard perception in novice and experienced drivers: The effects of sleepiness. Accident; Analysis and Prevention, 41(4), 729–733. https://doi.org/10.1016/j.aap.2009.03.016
  • Summala, H., Rajalin, S., & Radun, I. (2014). Risky driving and recorded driving offences: A 24-year follow-up study. Accident; Analysis and Prevention, 73, 27–33. https://doi.org/10.1016/j.aap.2014.08.008
  • Sun, Y. (2014). Research on road traffic accident analysis based on data mining. Jiaotong University %9 PhD.
  • Sun, Y., Shao, C., Hao, Y., & Liang, Z. (2014). An analysis of the factors influencing the severity of urban traffic accidents based on the sensitivity of SVM. Journal of Jilin University: Engineering Edition, (5), 1315–1320.
  • Walter, S. J., & Studdert, D. M. (2015). Relationship between penalties for road traffic infringements and crash risk in Queensland, Australia: a case-crossover study. International Journal of Epidemiology, 44(5), 1722–1730. https://doi.org/10.1093/ije/dyv148
  • Watson, B., Watson, A., Siskind, V., Fleiter, J., & Soole, D. (2015). Profiling high-range speeding offenders: Investigating criminal history, personal characteristics, traffic offences, and crash history. Accident; Analysis and Prevention, 74, 87–96. https://doi.org/10.1016/j.aap.2014.10.013
  • Wen, X., Xie, Y., Wu, L., & Jiang, L. (2021). Quantifying and comparing the effects of key risk factors on various types of roadway segment crashes with LightGBM and SHAP. Accident; Analysis and Prevention, 159, 106261. https://doi.org/10.1016/j.aap.2021.106261
  • Williams, A. F. (2006). Young driver risk factors: Successful and unsuccessful approaches for dealing with them and an agenda for the future. Injury Prevention: Journal of the International Society for Child and Adolescent Injury Prevention, 12 Suppl 1(suppl 1), i4–i8. https://doi.org/10.1136/ip.2006.011783
  • World Health Organization. (2022). Powered two-and three-wheeler safety: A road safety manual for decision-makers and practitioners (9240060561).
  • Wu, J. (2012). Advances in K-means clustering: A data mining thinking:. Springer Science & Business Media.
  • Xiao, D., Yuan, Q., Kang, S., Xu, X., & Wang, T. (2021). Insights on crash injury severity control from novice and experienced drivers: A bivariate random-effects probit analysis. Discrete Dynamics in Nature and Society, 2021, 1–13. https://doi.org/10.1155/2021/6675785
  • Yang, C. Y. D., & Najm, W. G. (2007). Examining driver behavior using data gathered from red light photo enforcement cameras. Journal of Safety Research, 38(3), 311–321. https://doi.org/10.1016/j.jsr.2007.01.008
  • Yao, S., Kronenburg, A., Shamooni, A., Stein, O. T., & Zhang, W. (2022). Gradient boosted decision trees for combustion chemistry integration. Applications in Energy and Combustion Science, 11, 100077. https://doi.org/10.1016/j.jaecs.2022.100077
  • Zhang, G. n., Yau, K. K., & Chen, G. (2013). Risk factors associated with traffic violations and accident severity in China. Accident; Analysis and Prevention, 59, 18–25. https://doi.org/10.1016/j.aap.2013.05.004
  • Zhang, J., Wang, Q., & Shen, W. (2022). Hyper-parameter optimization of multiple machine learning algorithms for molecular property prediction using hyperopt library. Chinese Journal of Chemical Engineering, 52, 115–125. https://doi.org/10.1016/j.cjche.2022.04.004
  • Zhang, Z. (2017). Analysis and modelling of traffic accident risk based on drivers’ traffic violations. Southeastern University.
  • Zhang, Z., He, Q., Gao, J., & Ni, M. (2018). A deep learning approach for detecting traffic accidents from social media data. Transportation Research Part C: Emerging Technologies, 86, 580–596. https://doi.org/10.1016/j.trc.2017.11.027
  • Zhou, B., Wang, X., Zhang, S., Li, Z., Sun, S., Shu, K., & Sun, Q. (2020). Comparing factors affecting injury severity of passenger car and truck drivers. IEEE Access. 8, 153849–153861. https://doi.org/10.1109/ACCESS.2020.3018183