Exploring injury severity of bicycle-motor vehicle crashes: A two-stage approach integrating latent class analysis and random parameter logit model

References

• Adanu, E. K., Hainen, A., & Jones, S. (2018). Latent class analysis of factors that influence weekday and weekend single-vehicle crash severities. Accident; Analysis and Prevention, 113, 187–192. doi:10.1016/j.aap.2018.01.035
   PubMed Web of Science ®Google Scholar
• Ahmed, M., Franke, R., Ksaibati, K., & Shinstine, D. (2018). Effects of truck traffic on crash injury severity on rural highways in Wyoming using Bayesian binary logit models. Accident; Analysis and Prevention, 117, 106–113. doi:10.1016/j.aap.2018.04.011
   PubMed Web of Science ®Google Scholar
• Arefkhani, H., Besharati, M. M., Bondarabadi, M. A., & Kashani, A. T. (2019). How does the incompatibility of different vehicle types affect the odds of driver injury? Journal of Transportation Safety & Security, 2, 1–17.
   Google Scholar
• Asgarzadeh, M., Verma, S., Mekary, R. A., Courtney, T. K., & Christiani, D. C. (2017). The role of intersection and street design on severity of bicycle-motor vehicle crashes. Injury Prevention: Journal of the International Society for Child and Adolescent Injury Prevention, 23(3), 179–185. doi:10.1136/injuryprev-2016-042045
   PubMed Web of Science ®Google Scholar
• Ball, L., Tully, R., & Egan, V. (2018). The influence of impulsivity and the Dark Triad on self-reported aggressive driving behaviours. Accident; Analysis and Prevention, 120, 130–138. doi:10.1016/j.aap.2018.08.010
   PubMed Web of Science ®Google Scholar
• Behnood, A., & Mannering, F. (2017). Determinants of cyclist injury severities in bicycle-vehicle crashes: A random parameters approach with heterogeneity in means and variances. Analytic Methods in Accident Research, 16, 35–47. doi:10.1016/j.amar.2017.08.001
   Web of Science ®Google Scholar
• Cao, J., Zhao, B., Gao, L., Li, J., Li, Z., & Zhao, X. (2018). Increasing temperature sensitivity caused by climate warming, evidence from Northeastern China. Dendrochronologia, 51, 101–111. doi:10.1016/j.dendro.2018.06.007
   Web of Science ®Google Scholar
• Celik, A. K., & Oktay, E. (2014). A multinomial logit analysis of risk factors influencing road traffic injury severities in the Erzurum and Kars Provinces of Turkey. Accident Analysis & Prevention, 72, 66–77. doi:10.1016/j.aap.2014.06.010
   PubMed Web of Science ®Google Scholar
• Chang, F., Xu, P., Zhou, H., Chan, A., & Huang, H. (2019). Investigating injury severities of motorcycle riders: A two-step method integrating latent class cluster analysis and random parameters logit model. Accident; Analysis and Prevention, 131, 316–326. doi:10.1016/j.aap.2019.07.012
   PubMed Web of Science ®Google Scholar
• Chen, P., & Shen, Q. (2019). Identifying high-risk built environments for severe bicycling injuries. Journal of Safety Research, 68, 1–7. doi:10.1016/j.jsr.2018.11.002
   PubMed Web of Science ®Google Scholar
• Cheng, W., Gill, G. S., Sakrani, T., Dasu, M., & Zhou, J. (2017). Predicting motorcycle crash injury severity using weather data and alternative Bayesian multivariate crash frequency models. Accident; Analysis and Prevention, 108, 172–180. doi:10.1016/j.aap.2017.08.032
   PubMed Web of Science ®Google Scholar
• Chu, H. (2012). An investigation of the risk factors causing severe injuries in crashes involving gravel trucks. Traffic Injury Prevention, 13(4), 355–363. doi:10.1080/15389588.2012.654545
   PubMed Web of Science ®Google Scholar
• Dai, S., Liu, J., & Zhao, L. (2020). Research report on metropolis’ road traffic development in China. Beijing, China: China Communications Press.
   Google Scholar
• Depaire, B., Wets, G., & Vanhoof, K. (2008). Traffic accident segmentation by means of latent class clustering. Accident; Analysis and Prevention, 40(4), 1257–1266. doi:10.1016/j.aap.2008.01.007
   PubMed Web of Science ®Google Scholar
• Dong, C., Shao, C., Clarke, D. B., & Nambisan, S. S. (2018). An innovative approach for traffic crash estimation and prediction on accommodating unobserved heterogeneities. Transportation Research Part B: Methodological, 118, 407–428. doi:10.1016/j.trb.2018.10.020
   Web of Science ®Google Scholar
• Guo, Y., Li, Z., Liu, P., & Wu, Y. (2019). Modeling correlation and heterogeneity in crash rates by collision types using full Bayesian random parameters multivariate Tobit model. Accident; Analysis and Prevention, 128, 164–174. doi:10.1016/j.aap.2019.04.013
   PubMed Web of Science ®Google Scholar
• Haleem, K., & Gan, A. (2013). Effect of driver’s age and side of impact on crash severity along urban freeways: A mixed logit approach. Journal of Safety Research, 46, 67–76. doi:10.1016/j.jsr.2013.04.002
   PubMed Web of Science ®Google Scholar
• Heejin, J., Jang, Y., Bowman, P. J., & Masoud, N. (2018). Classification of motor vehicle crash injury severity: A hybrid approach for imbalanced data. Accident Analysis & Prevention, 120, 250–261.
   PubMed Web of Science ®Google Scholar
• Isaksson-Hellman, I., & Toreki, J. (2019). The effect of speed limit reductions in urban areas on cyclists' injuries in collisions with cars. Traffic Injury Prevention, 20(sup3), 39–36. doi:10.1080/15389588.2019.1680836
   PubMed Web of Science ®Google Scholar
• Kamel, M., & Sayed, T. (2021). Accounting for seasonal effects on cyclist-vehicle crashes. Accident; Analysis and Prevention, 159, 106263. doi:10.1016/j.aap.2021.106263
   PubMed Web of Science ®Google Scholar
• Kamel, M., Sayed, T., & Osama, A. (2019). Accounting for mediation in cyclist-vehicle crash models: A Bayesian mediation analysis approach. Accident; Analysis and Prevention, 131, 122–130. doi:10.1016/j.aap.2019.06.009
   PubMed Web of Science ®Google Scholar
• Kim, S.-C., Lee, H.-J., Kim, J.-M., Kong, S.-Y., Park, J.-S., Jeon, H.-J., In, Y.-N., Kim, H., Lee, S.-W., & Kim, Y.-T. (2019). Comparison of epidemiology and injury profile between vulnerable road users and motor vehicle occupants in road traffic fatalities. Traffic Injury Prevention, 20(6), 581–587. doi:10.1080/15389588.2018.1539840
   PubMed Web of Science ®Google Scholar
• Li, L., Hasnine, M., Habib, K., Persaud, B., & Shalaby, A. (2017). Investigating the interplay between the attributes of at-fault and not-at-fault drivers and the associated impacts on crash injury occurrence and severity level. Journal of Transportation Safety & Security, 9(4), 439–456. doi:10.1080/19439962.2016.1237602
   Web of Science ®Google Scholar
• Li, W., Gkritza, K., & Albrecht, C. (2014). The culture of distracted driving: Evidence from a public opinion survey in Iowa. Transportation Research Part F: Traffic Psychology and Behaviour, 26, 337–347. doi:10.1016/j.trf.2014.01.002
   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 and Prevention, 131, 284–296. doi:10.1016/j.aap.2019.07.008
   PubMed Web of Science ®Google Scholar
• Li, Y., & Fernie, G. (2010). Pedestrian behavior and safety on a two-stage crossing with a center refuge island and the effect of winter weather on pedestrian compliance rate. Accident; Analysis and Prevention, 42(4), 1156–1163. doi:10.1016/j.aap.2010.01.004
   PubMed Web of Science ®Google Scholar
• Li, Z., Wang, W., Yang, C., & Ding, H. (2017). Bicycle mode share in China: A city-level analysis of long term trends. Transportation, 44(4), 773–788. doi:10.1007/s11116-016-9676-8
   Web of Science ®Google Scholar
• Li, Z., Wu, Q., Ci, Y., Chen, C., Chen, X., & Zhang, G. (2019). Using latent class analysis and mixed logit model to explore risk factors on driver injury severity in single-vehicle crashes. Accident; Analysis and Prevention, 129, 230–240. doi:10.1016/j.aap.2019.04.001
   PubMed Web of Science ®Google Scholar
• Lin, Z., & Fan, W. (2019). Modeling bicyclist injury severity in bicycle-motor vehicle crashes that occurred in urban and rural areas: A mixed logit analysis. Canadian Journal of Civil Engineering, 46(10), 924–933. doi:10.1139/cjce-2018-0781
   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 and Prevention, 135, 105388. doi:10.1016/j.aap.2019.105388
   PubMed Web of Science ®Google Scholar
• Malyshkina, N., & Mannering, F. (2009). Markov switching multinomial logit model: An application to accident-injury severities. Accident; Analysis and Prevention, 41(4), 829–838. doi:10.1016/j.aap.2009.04.006
   PubMed Web of Science ®Google Scholar
• Mannering, F., & Bhat, C. (2014). Analytic methods in accident research: methodological frontier and future directions. Analytic Methods in Accident Research, 1, 1–22.
   Google Scholar
• McFadden, D., & Train, K. (2000). Mixed MNL models for discrete response. Journal of Applied Econometrics, 15(5), 447–470. doi:10.1002/1099-1255(200009/10)15:5<447::AID-JAE570>3.0.CO;2-1
   Web of Science ®Google Scholar
• Meuleners, L. B., Fraser, M., Johnson, M., Stevenson, M., Rose, G., & Oxley, J. (2020). Characteristics of the road infrastructure and injurious cyclist crashes resulting in a hospitalisation. Accident; Analysis and Prevention, 136, 105407. doi:10.1016/j.aap.2019.105407
   PubMed 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. doi:10.1016/j.ssci.2012.11.001
   Web of Science ®Google Scholar
• Moore, D. N., Schneider, W. H., Savolainen, P. T., & Farzaneh, M. (2011). Mixed logit analysis of bicyclist injury severity resulting from motor vehicle crashes at intersection and non-intersection locations. Accident; Analysis and Prevention, 43(3), 621–630. doi:10.1016/j.aap.2010.09.015
   PubMed Web of Science ®Google Scholar
• Naqvi, H. M., & Tiwari, G. (2017). Factors contributing to motorcycle fatal crashes on national highways in India. Transportation Research Procedia, 25, 2084–2102. doi:10.1016/j.trpro.2017.05.402
   Google Scholar
• Rezapour, M., Wulff, S. S., & Ksaibati, K. (2019). Examination of the severity of two-lane highway traffic barrier crashes using the mixed logit model. Journal of Safety Research, 70, 223–232. doi:10.1016/j.jsr.2019.07.010
   PubMed Web of Science ®Google Scholar
• Shen, S., & Neyens, D. (2015). The effects of age, gender, and crash types on drivers' injury-related health care costs. Accident; Analysis and Prevention, 77, 82–90. doi:10.1016/j.aap.2015.01.014
   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.
   Google Scholar
• Sivasankaran, S. K., & Balasubramanian, V. (2020). Exploring the severity of bicycle-vehicle crashes using latent class clustering approach in India. Journal of Safety Research, 72, 127–138. doi:10.1016/j.jsr.2019.12.012
   PubMed Web of Science ®Google Scholar
• Stipancic, J., Zangenehpour, S., Miranda-Moreno, L., Saunier, N., & Granie, M. A. (2016). Investigating the gender differences on bicycle-vehicle conflicts at urban intersections using an ordered logit methodology. Accident Analysis & Prevention, 97, 19–27. doi:10.1016/j.aap.2016.07.033
   PubMed Web of Science ®Google Scholar
• Sun, M., Sun, X., & Shan, D. (2019). Pedestrian crash analysis with latent class clustering method. Accident; Analysis and Prevention, 124, 50–57. doi:10.1016/j.aap.2018.12.016
   PubMed Web of Science ®Google Scholar
• Uebersax, J. S., & Grove, W. M. (1990). Latent class analysis of diagnostic agreement. Statistics in Medicine, 9(5), 559–572. doi:10.1002/sim.4780090509
   PubMed Web of Science ®Google Scholar
• Weiss, H., Kaplan, S., & Prato, C. (2016). Fatal and serious road crashes involving young New Zealand drivers: A latent class clustering approach. International Journal of Injury Control and Safety Promotion, 23(4), 427–443. doi:10.1080/17457300.2015.1056807
   PubMed Web of Science ®Google Scholar
• Weng, J., Gan, X., & Chen, J. (2021). A separate analysis of crash frequency for the highways involving traffic hazards and involving no traffic hazards. Journal of Transportation Safety & Security, 13(8), 822–841.
   Web of Science ®Google Scholar
• Yan, X., & Radwan, E. (2006). Analyses of rear-end crashes based on classification tree models. Traffic Injury Prevention, 7(3), 276–282. doi:10.1080/15389580600660062
   PubMedGoogle Scholar
• Yasmin, S., & Eluru, N. (2016). Latent segmentation based count models: Analysis of bicycle safety in Montreal and Toronto. Accident; Analysis and Prevention, 95(Pt A), 157–171. doi:10.1016/j.aap.2016.07.015
   PubMedGoogle Scholar
• Zhang, G., Yau, K., & Chen, G. (2013). Risk factors associated with traffic violations and accident severity in China. Accident; Analysis and Prevention, 59, 18–25. doi:10.1016/j.aap.2013.05.004
   PubMed Web of Science ®Google Scholar
• Zhu, S. (2020). Investigation of vehicle-bicycle hit-and-run crashes. Traffic Injury Prevention, 21(7), 506–511. doi:10.1080/15389588.2020.1805444
   PubMed Web of Science ®Google Scholar
• Zubaidi, H., Obaid, L., Alnedawi, A., & Das, S. (2021). Motor vehicle driver injury severity analysis utilizing a random parameter binary probit model considering different types of driving licenses in 4-legs roundabouts in South Australia. Safety Science, 134, 105083. doi:10.1016/j.ssci.2020.105083
   Web of Science ®Google Scholar