Exploring the heterogeneities in vehicle-involved traffic violations at intersections using latent class clustering and partial proportional odds models

References

• Akaateba, M. A., Amoh-Gyimah, R., & Amponsah, O. (2015). Traffic safety violations in relation to drivers’ educational attainment, training and experience in Kumasi, Ghana. Safety Science, 75, 156–162. doi:10.1016/j.ssci.2015.02.010
   Web of Science ®Google Scholar
• Atombo, C., Wu, C., Zhong, M., & Zhang, H. (2016). Investigating the motivational factors influencing drivers intentions to unsafe driving behaviours: Speeding and overtaking violations. Transportation Research Part F: Traffic Psychology and Behaviour, 43, 104–121. doi:10.1016/j.trf.2016.09.029
   Web of Science ®Google Scholar
• Azad, P., Halvani, G. H., Najimi, M. R., & Kouhnavard, B. (2015). Investigating the role of behavioral factors in non-fatal accidents of urban and suburban driver. Journal of Health and Safety at Work, 5(3), 13–20. https://jhsw.tums.ac.ir/article-1-5299-en.html.
   Web of Science ®Google Scholar
• Azimi, G., Rahimi, A., Asgari, H., & Jin, X. (2020). Severity analysis for large truck rollover crashes using a random parameter ordered logit model. Accident Analysis & Prevention, 135, 105355. doi:10.1016/j.aap.2019.105355
   PubMed Web of Science ®Google Scholar
• Bijmolt, T. H., Paas, L. J., & Vermunt, J. K. (2004). Country and consumer segmentation: Multi-level latent class analysis of financial product ownership. International Journal of Research in Marketing, 21(4), 323–340. doi:10.1016/j.ijresmar.2004.06.002
   Web of Science ®Google Scholar
• Castellà, J., & Pérez, J. (2004). Sensitivity to punishment and sensitivity to reward and traffic violations. Accident Analysis & Prevention, 36(6), 947–952. doi:10.1016/j.aap.2003.10.003
   PubMed Web of Science ®Google Scholar
• Chen, W., & Zhu, F. (2022). Discussions on pedestrian delay models and applications at signalized crosswalks. Multimodal Transportation, 1(4), 100039. doi:10.1016/j.multra.2022.100039
   Google Scholar
• Cheng, Z., Lu, J., Zu, Z., & Li, Y. (2019). Speeding violation type prediction based on decision tree method: A case study in Wujiang, China. Journal of Advanced Transportation, 2019, 1–10. doi:10.1155/2019/8650845
   Web of Science ®Google Scholar
• Depaire, B., Wets, G., & Vanhoof, K. (2008). Traffic accident segmentation by means of latent class clustering. Accident Analysis & Prevention, 40(4), 1257–1266. doi:10.1016/j.aap.2008.01.007
   PubMed Web of Science ®Google Scholar
• Ding, H., & Sze, N. N. (2022). Effects of road network characteristics on bicycle safety: A multivariate Poisson-lognormal model. Multimodal Transportation, 1(2), 100020. doi:10.1016/j.multra.2022.100020
   Google Scholar
• Dommes, A., Granie, M. A., Cloutier, M. S., Coquelet, C., & Huguenin-Richard, F. (2015). Red light violations by adult pedestrians and other safety-related behaviors at signalized crosswalks. Accident Analysis & Prevention, 80, 67–75. doi:10.1016/j.aap.2015.04.002
   PubMed Web of Science ®Google Scholar
• Guo, Y., Li, Z., Wu, Y., & Xu, C. (2018). Exploring unobserved heterogeneity in bicyclists’ red-light running behaviors at different crossing facilities. Accident Analysis & Prevention, 115, 118–127. doi:10.1016/j.aap.2018.03.006
   PubMed Web of Science ®Google Scholar
• Guo, Y., Osama, A., & Sayed, T. (2018). A cross-comparison of different techniques for modeling macro-level cyclist crashes. Accident Analysis & Prevention, 113, 38–46.
   PubMed Web of Science ®Google Scholar
• Guo, Y., Sayed, T., & Zheng, L. (2020). A hierarchical bayesian peak over threshold approach for conflict-based before-after safety evaluation of leading pedestrian intervals. Accident Analysis & Prevention, 147, 105772. doi:10.1016/j.aap.2020.105772
   PubMed Web of Science ®Google Scholar
• Hong, J., Park, J., Lee, G., & Park, D. (2019). Endogenous commercial driver’s traffic violations and freight truck-involved crashes on mainlines of expressway. Accident Analysis & Prevention, 131, 327–335. doi:10.1016/j.aap.2019.07.026
   PubMed Web of Science ®Google Scholar
• Hosseinlou, M. H., Mahdavi, A., & Nooghabi, M. J. (2018). Validation of the influencing factors associated with traffic violations and crashes on freeways of developing countries: A case study of Iran. Accident Analysis & Prevention, 121, 358–366. doi:10.1016/j.aap.2018.06.009
   PubMed Web of Science ®Google Scholar
• Hu, W., & Donnell, E. T. (2011). Severity models of cross-median and rollover crashes on rural divided highways in Pennsylvania. Journal of Safety Research, 42(5), 375–382. doi:10.1016/j.jsr.2011.07.004
   PubMed Web of Science ®Google Scholar
• Jahangiri, M., Aghamolaei, T., Mohseni, S., Mohammadpour, S., Sallari, N., & Tajvar, A. (2019). Investigating self-reported violations of the drivers of Bandar Abbas city and its relation with their knowledge and attitude regarding traffic regulations. Archives of Trauma Research, 8(4), 219–228. doi:10.4103/atr.atr_31_19
   Web of Science ®Google Scholar
• Jiang, X., Huang, K., Wang, B., & Wang, Y. (2013). Analysis of affecting factors on traffic violation under the environment of electronic enforcement. Journal of Harbin Institute of Technology, 45(8), 84–89. doi:10.11918/j.issn.0367-6234
   Google Scholar
• Joewono, T. B., & Susilo, Y. O. (2017). Traffic violations by young motorcyclists on Indonesian urban roads. Journal of Transportation Safety & Security, 9(sup1), 236–261. doi:10.1080/19439962.2016.1247123
   Web of Science ®Google Scholar
• Lanza, S. T., & Rhoades, B. L. (2013). Latent class analysis: An alternative perspective on subgroup analysis in prevention and treatment. Prevention Science, 14(2), 157–168. doi:10.1007/s11121-011-0201-1
   PubMed Web of Science ®Google Scholar
• Li, Y., Abdel-Aty, M., Yuan, J., Cheng, Z., & Lu, J. (2020). Analyzing traffic violation behavior at urban intersections: A spatio-temporal kernel density estimation approach using automated enforcement system data. Accident; Analysis and Prevention, 141, 105509 doi:10.1016/j.aap.2020.105509
   PubMed Web of Science ®Google Scholar
• Li, Y., & Fan, W. (2019). Modelling severity of pedestrian-injury in pedestrian-vehicle crashes with latent class clustering and partial proportional odds model: A case study of North Carolina. Accident Analysis & Prevention, 131, 284–296. doi:10.1016/j.aap.2019.07.008
   PubMed Web of Science ®Google Scholar
• Liang, C., Ghazel, M., Cazier, O., & El-Koursi, E.-M. (2017). A new insight on the risky behavior of motorists at railway level crossings: An observational field study. Accident Analysis & Prevention, 108, 181–188. doi:10.1016/j.aap.2017.08.030
   PubMed Web of Science ®Google Scholar
• Lin, Z., & Fan, W. (2021). Exploring bicyclist injury severity in bicycle-vehicle crashes using latent class clustering analysis and partial proportional odds models. Journal of Safety Research, 76, 101–117. doi:10.1016/j.jsr.2020.11.012
   PubMed Web of Science ®Google Scholar
• Liu, P., & Fan, W. (2019). Analysis of head-on crash injury severity using a partial proportional odds model. Journal of Transportation Safety & Security, 13(7),1–21. doi:10.1080/19439962.2019.1667933
   Web of Science ®Google Scholar
• Liu, P., & Fan, W. (2020). Exploring injury severity in head-on crashes using latent class clustering analysis and mixed logit model: A case study of North Carolina. Accident Analysis & Prevention, 135, 105388. doi:10.1016/j.aap.2019.105388
   PubMed Web of Science ®Google Scholar
• Lv, L., & Chen, D. (2021). Impact of climate comfort of urban human settlements on urban innovation. Areal Research and Development, 40(2), 45–49. doi:10.3969/j.issn.1003-2363.2021.02.008
   Google Scholar
• Mahajan, K., Velaga, N. R., Kumar, A., & Choudhary, P. (2019). Effects of driver sleepiness and fatigue on violations among truck drivers in India. International Journal of Injury Control and Safety Promotion, 26(4), 412–422. doi:10.1080/17457300.2019.1660375
   PubMed Web of Science ®Google Scholar
• Mane, A. S., & Pulugurtha, S. S. (2018). Influence of on-network, traffic, signal, demographic, and land use characteristics by area type on red light violation crashes. Accident Analysis & Prevention, 120, 101–113. doi:10.1016/j.aap.2018.08.006
   PubMed Web of Science ®Google Scholar
• Martínez-Ruíz, D. M., Fandino-Losada, A., Ponce De Leon, A., Arango-Londono, D., Mateus, J. C., Jaramillo-Molina, C., Bonilla-Escobar, F. J., Vivas, H., Vanlaar, W., & Gutierrez-Martinez, M. I. (2019). Impact evaluation of camera enforcement for traffic violations in Cali, Colombia, 2008-2014. Accident Analysis & Prevention, 125, 267–274. doi:10.1016/j.aap.2019.02.002
   PubMed Web of Science ®Google Scholar
• McCartt, A. T., & Hu, W. (2014). Effects of red light camera enforcement on red light violations in Arlington County, Virginia. Journal of Safety Research, 48, 57–62. doi:10.1016/j.jsr.2013.12.001
   PubMed Web of Science ®Google Scholar
• Ministry of Transport of the People’s Republic of China. (2019). Statistical bulletin of the development of the transportation industry. Analysis Bulletin 2019, Integrated Planning Division, Government of the People’s Republic of China. http://www.gov.cn/xinwen/2020-05/12/content_5510817.htm
   Google Scholar
• Mukherjee, D., & Mitra, S. (2020). A comprehensive study on factors influencing pedestrian signal violation behaviour: Experience from Kolkata City, India. Safety Science, 124, 104610. doi:10.1016/j.ssci.2020.104610
   Web of Science ®Google Scholar
• Rudisill, T. M., & Zhu, M. (2016). Who actually receives cell phone use while driving citations and how much are these laws enforced among states? A descriptive, cross-sectional study. BMJ Open, 6(6), e011381. doi:10.1136/bmjopen-2016-011381
   PubMed Web of Science ®Google Scholar
• Sasidharan, L., Wu, K.-F., & Menendez, M. (2015). Exploring the application of latent class cluster analysis for investigating pedestrian crash injury severities in Switzerland. Accident Analysis & Prevention, 85, 219–228. doi:10.1016/j.aap.2015.09.020
   PubMed Web of Science ®Google Scholar
• Shell, D. F., Newman, I. M., Cordova-Cazar, A. L., & Heese, J. M. (2015). Driver education and teen crashes and traffic violations in the first two years of driving in a graduated licensing system. Accident Analysis & Prevention, 82, 45–52. doi:10.1016/j.aap.2015.05.011
   PubMed Web of Science ®Google Scholar
• Song, L., & Fan, W. (2020). Combined latent class and partial proportional odds model approach to exploring the heterogeneities in truck-involved severities at cross and T-intersections. Accident Analysis & Prevention, 144, 105638. doi:10.1016/j.aap.2020.105638
   PubMed Web of Science ®Google Scholar
• Stanitsa, A., Hallett, H., & Jude, S. (2022). Investigating pedestrian behaviour in urban environments: A Wi-Fi tracking and machine learning approach. Multimodal Transportation, 2(1),100049.
   Google Scholar
• Sun, M., Sun, X., & Shan, D. (2019). Pedestrian crash analysis with latent class clustering method. Accident Analysis & Prevention, 124, 50–57. doi:10.1016/j.aap.2018.12.016
   PubMed Web of Science ®Google Scholar
• Uddin, M., & Huynh, N. (2017). Truck-involved crashes injury severity analysis for different lighting conditions on rural and urban roadways. Accident Analysis & Prevention, 108, 44–55. doi:10.1016/j.aap.2017.08.009
   PubMed Web of Science ®Google Scholar
• Wang, Q., Zhang, W., Yang, R., Huang, Y., Zhang, L., Ning, P., Cheng, X., Schwebel, D. C., Hu, G., & Yao, H. (2015). Common traffic violations of bus drivers in urban China: An observational study. PLoS One, 10(9), e0137954. doi:10.1371/journal.pone.0137954
   Web of Science ®Google Scholar
• Wang, Y., & Prato, C. G. (2019). Determinants of injury severity for truck crashes on mountain expressways in China: A case-study with a partial proportional odds model. Safety Science, 117, 100–107. doi:10.1016/j.ssci.2019.04.011
   Web of Science ®Google Scholar
• Wu, J., Fan, W., & Wang, W. (2020). A zero-inflated ordered probit model to analyze hazmat truck drivers’ violation behavior and associated risk factors. IEEE Access, 8, 110974–110985. doi:10.1109/ACCESS.2020.3001165
   Web of Science ®Google Scholar
• Wu, Q., Zhang, G. H., Zhu, X. Y., Liu, X. C., & Tarefder, R. (2016). Analysis of driver injury severity in single-vehicle crashes on rural and urban roadways. Accident Analysis & Prevention, 94, 35–45. doi:10.1016/j.aap.2016.03.026
   PubMed Web of Science ®Google Scholar
• Yadav, R., Zhang, W., Kaiwartya, O., Singh, P. R., Elgendy, I. A., & Tian, Y.-C. (2018). Adaptive energy-aware algorithms for minimizing energy consumption and SLA violation in cloud computing. IEEE Access, 6, 55923–55936. doi:10.1109/ACCESS.2018.2872750
   Web of Science ®Google Scholar
• Yoh, K., Okamoto, T., Inoi, H., & Doi, K. (2017). Comparative study on foreign drivers’ characteristics using traffic violation and accident statistics in Japan. IATSS Research, 41(2), 94–105. doi:10.1016/j.iatssr.2017.06.004
   Google Scholar
• Zahid, M., Chen, Y., Khan, S., Jamal, A., Ijaz, M., & Ahmed, T. (2020). Predicting risky and aggressive driving behavior among taxi drivers: Do spatio-temporal attributes matter? International Journal of Environmental Research and Public Health, 17(11), 3937. doi:10.3390/ijerph17113937
   PubMed Web of Science ®Google Scholar
• Zeng, Q., Gu, W. H., Zhang, X., Wen, H. Y., Lee, J., & Hao, W. (2019). Analyzing freeway crash severity using a Bayesian spatial generalized ordered logit model with conditional autoregressive priors. Accident Analysis & Prevention, 127, 87–95. doi:10.1016/j.aap.2019.02.029
   PubMed Web of Science ®Google Scholar
• Zhang, G., Yau, K. K., & Gong, X. (2014). Traffic violations in Guangdong province of China: Speeding and drunk driving. Accident Analysis & Prevention, 64, 30–40. doi:10.1016/j.aap.2013.11.002
   PubMed Web of Science ®Google Scholar
• Zhao, J., Li, P., Zheng, Z., & Han, Y. (2018). Analysis of saturation flow rate at tandem intersections using field data. IET Intelligent Transport Systems, 12(5), 394–403. doi:10.1049/iet-its.2017.0092
   Web of Science ®Google Scholar