Differences in single-vehicle motorcycle crashes caused by distraction and overspeed behaviors: considering temporal shifts and unobserved heterogeneity in prediction

References

• Al-Bdairi, N., Behnood, A., & Hernandez, S. (2020). Temporal stability of driver injury severities in animal-vehicle collisions: A random parameters with heterogeneity in means (and variances) approach. Analytic Methods in Accident Research, 26, 100120. https://doi.org/10.1016/j.amar.2020.100120
   Web of Science ®Google Scholar
• Alnawmasi, N., & Mannering, F. (2019). A statistical assessment of temporal instability in the factors determining motorcyclist injury severities. Analytic Methods in Accident Research, 22, 100090. 1–20. https://doi.org/10.1016/j.amar.2019.100090
   Web of Science ®Google Scholar
• Alnawmasi, N., & Mannering, F. (2022). The impact of higher speed limits on the frequency and severity of freeway crashes: Accounting for temporal shifts and unobserved heterogeneity. Analytic Methods in Accident Research, 34, 100205. https://doi.org/10.1016/j.amar.2021.100205
   Web of Science ®Google Scholar
• Alogaili, A., & Mannering, F. (2020). Unobserved heterogeneity and the effects of driver nationality on crash injury severities in Saudi Arabia. Accident; Analysis and Prevention, 144, 105618. https://doi.org/10.1016/j.aap.2020.105618
   PubMed Web of Science ®Google Scholar
• Alogaili, A., & Mannering, F. (2022). Differences between day and night pedestrian-injury severities: Accounting for temporal and unobserved effects in prediction. Analytic Methods in Accident Research, 33, 100201. https://doi.org/10.1016/j.amar.2021.100201
   Web of Science ®Google Scholar
• Behnood, A., & Al-Bdairi, N. (2020). Determinant of injury severities in large truck crashes: A weekly instability analysis. Safety Science, 131, 104911. https://doi.org/10.1016/j.ssci.2020.104911
   Web of Science ®Google Scholar
• Behnood, A., & Mannering, F. (2015). The temporal stability of factors affecting driver-injury severities in single-vehicle crashes: Some empirical evidence. Analytic Methods in Accident Research, 8, 7–32. https://doi.org/10.1016/j.amar.2015.08.001
   Web of Science ®Google Scholar
• Behnood, A., & Mannering, F. (2016). An empirical assessment of the effects of economic recessions on pedestrian-injury crashes using mixed and latent-class models. Analytic Methods in Accident Research, 12, 1–17. https://doi.org/10.1016/j.amar.2016.07.002
   Web of Science ®Google Scholar
• Behnood, A., & Mannering, F. (2017). Determinants of bicyclist injury severities in bicycle-vehicle crashes: A random parameters approach with heterogeneity in means and variances. Analytic Methods in Accident Research, 16, 35–47. https://doi.org/10.1016/j.amar.2017.08.001
   Web of Science ®Google Scholar
• Behnood, A., & Mannering, F. (2019). Time-of-day variations and temporal instability of factors affecting injury severities in large-truck crashes. Analytic Methods in Accident Research, 23, 100102. https://doi.org/10.1016/j.amar.2019.100102
   Web of Science ®Google Scholar
• Chang, F., Li, M., Xu, P., Zhou, H., Haque, M., & Huang, H. (2016). Injury severity of motorcycle riders involved in traffic crashes in Hunan, China: A mixed ordered logit approach. International Journal of Environmental Research and Public Health, 13(7), 714. https://doi.org/10.3390/ijerph13070714
   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. https://doi.org/10.1016/j.aap.2019.07.012
   PubMed Web of Science ®Google Scholar
• Chang, F., Yasmin, S., Huang, H., Chan, A., & Haque, M. (2021). Injury severity analysis of motorcycle crashes: A comparison of latent class clustering and latent segmentation based models with unobserved heterogeneity. Analytic Methods in Accident Research, 32, 100188. https://doi.org/10.1016/j.amar.2021.100188
   Web of Science ®Google Scholar
• Council, F., Reurings, M., Srinivasan, R., Maten, S., & Carter, D. (2010). Development of a speed-related crash typology. FHWA-HRT-10-024. McLean, VA.
   Google Scholar
• Cunto, F., & Ferreira, S. (2017). An analysis of the injury severity of motorcycle crashes in Brazil using mixed ordered response models. Journal of Transportation Safety and Security, 9(sup1), 33–46. https://doi.org/10.1080/19439962.2016.1162891
   Web of Science ®Google Scholar
• De Lapparent, M. (2006). Empirical Bayesian analysis of accident severity for motorcyclists in large French urban areas. Accident; Analysis and Prevention, 38(2), 260–268. https://doi.org/10.1016/j.aap.2005.09.001
   PubMed Web of Science ®Google Scholar
• Donmez, B., & Liu, Z. (2015). Associations of distraction involvement and age with driver injury severities. Journal of Safety Research, 52, 23–28. https://doi.org/10.1016/j.jsr.2014.12.001
   PubMed Web of Science ®Google Scholar
• Hou, Q., Huo, X., Leng, J., & Mannering, F. (2022). A note on out-of-sample prediction, marginal effects computations, and temporal testing with random parameters crash-injury severity models. Analytic Methods in Accident Research, 33, 100191. https://doi.org/10.1016/j.amar.2021.100191
   Web of Science ®Google Scholar
• Ijaz, M., Lan, L., Usman, S., Zahid, M., & Jamal, A. (2022). Investigation of factors influencing motorcyclist injury severity using random parameters logit model with heterogeneity in means and variances. International Journal of Crashworthiness, 27(5), 1412–1422. https://doi.org/10.1080/13588265.2021.1959153
   Web of Science ®Google Scholar
• Islam, M. (2021). The effect of motorcyclists’ age on injury severities in single-motorcycle crashes with unobserved heterogeneity. Journal of Safety Research, 77, 125–138. https://doi.org/10.1016/j.jsr.2021.02.010
   PubMed Web of Science ®Google Scholar
• Islam, M., Alnawmasi, N., & Mannering, F. (2020). Unobserved heterogeneity and temporal instability in the analysis of work-zone crash-injury severities. Analytic Methods in Accident Research, 28, 100130. https://doi.org/10.1016/j.amar.2020.100130
   Web of Science ®Google Scholar
• Islam, M., & Mannering, F. (2020). A temporal analysis of driver-injury severities in crashes involving aggressive and non-aggressive driving. Analytic Methods in Accident Research, 27, 100128. https://doi.org/10.1016/j.amar.2020.100128
   Web of Science ®Google Scholar
• Islam, M., & Mannering, F. (2021). The role of gender and temporal instability in driver-injury severities in crashes caused by speeds too fast for conditions. Accident; Analysis and Prevention, 153, 106039. https://doi.org/10.1016/j.aap.2021.106039
   PubMed Web of Science ®Google Scholar
• Jung, S., Xiao, Q., & Yoon, Y. (2013). Evaluation of motorcycle safety strategies using the severity of injuries. Accident; Analysis and Prevention, 59, 357–364. https://doi.org/10.1016/j.aap.2013.06.030
   PubMed Web of Science ®Google Scholar
• Khan, I., & Vachal, K. (2020). Factors affecting injury severity of single-vehicle rollover crashes in the United States. Traffic Injury Prevention, 21(1), 66–71. https://doi.org/10.1080/15389588.2019.1696962
   PubMed Web of Science ®Google Scholar
• Lin, M., Chang, S., Huang, W., Hwang, H., & Pai, L. (2003). Factors associated with severity of motorcycle injuries among young adult riders. Annals of Emergency Medicine, 41(6), 783–791. https://doi.org/10.1067/mem.2003.186
   PubMed Web of Science ®Google Scholar
• Li, J., Fang, S., Guo, J., Fu, T., & Qiu, M. (2021). A motorcyclist-injury severity analysis: A comparison of single-, two-, and multi-vehicle crashes using latent class ordered probit model. Accident; Analysis and Prevention, 151, 105953. https://doi.org/10.1016/j.aap.2020.105953
   PubMed Web of Science ®Google Scholar
• Li, X., Liu, J., Zhang, Z., Parrish, A., & Jones, S. (2021). A spatiotemporal analysis of motorcyclist injury severity: Findings from 20 years of crash data from Pennsylvania. Accident; Analysis and Prevention, 151, 105952. https://doi.org/10.1016/j.aap.2020.105952
   PubMed Web of Science ®Google Scholar
• Li, Y., Song, L., & David, W. (2021). Day-of-the-week variations and temporal instability of factors influencing pedestrian injury severity in pedestrian-vehicle crashes: A random parameters logit approach with heterogeneity in means and variances. Analytic Methods in Accident Research, 29, 100152. https://doi.org/10.1016/j.amar.2020.100152
   Web of Science ®Google Scholar
• Mannering, F. (2018). Temporal instability and the analysis of highway accident data. Analytic Methods in Accident Research, 17, 1–13. https://doi.org/10.1016/j.amar.2017.10.002
   Web of Science ®Google Scholar
• Mannering, F., & Bhat, C. R. (2014). Analytic methods in accident research: Methodological frontier and future directions. Analytic Methods in Accident Research, 1, 1–22. https://doi.org/10.1016/j.amar.2013.09.001
   Google Scholar
• Mannering, F., Shankar, V., & Bhat, C. (2016). Unobserved heterogeneity and the statistical analysis of highway accident data. Analytic Methods in Accident Research, 11, 1–16. https://doi.org/10.1016/j.amar.2016.04.001
   Web of Science ®Google Scholar
• McFadden, D., & Train, K. (2000). Mixed MNL models for discrete response. Journal of Applied Econometrics, 15(5), 447–470. https://doi.org/10.1002/1099-1255(200009/10)15:5<447::AID-JAE570>3.0.CO;2-1
   Web of Science ®Google Scholar
• Meng, F., Sze, N., Song, C., Chen, T., & Zeng, Y. (2021). Temporal instability of truck volume composition on non-truck-involved crash severity using uncorrelated and correlated grouped random parameters binary logit models with space-time variations. Analytic Methods in Accident Research, 31, 100168. https://doi.org/10.1016/j.amar.2021.100168
   Web of Science ®Google Scholar
• Milton, J., Shankar, V., & Mannering, F. (2008). Highway accident severities and the mixed logit model: An exploratory empirical analysis. Accident; Analysis and Prevention, 40(1), 260–266. https://doi.org/10.1016/j.aap.2007.06.006
   PubMed Web of Science ®Google Scholar
• Obaid, I., Alnedawi, A., Aboud, G. M., Tamakloe, R., Zuabidi, H., & Das, S. (2022). Factors associated with driver injury severity of motor vehicle crashes on sealed and unsealed pavements: Random parameter model with heterogeneity in means and variances. International Journal of Transportation Science and Technology, 267 https://doi.org/10.1016/j.ijtst.2022.04.002
   Google Scholar
• Pai, C., & Saleh, W. (2007). An analysis of motorcyclist injury severity under various traffic control measures at three-legged junctions in the UK. Safety Science, 45(8), 832–847. https://doi.org/10.1016/j.ssci.2006.08.021
   Web of Science ®Google Scholar
• Pai, C., & Saleh, W. (2008). Modelling motorcyclist injury severity by various crash types at T-junctions in the UK. Safety Science, 46(8), 1234–1247. https://doi.org/10.1016/j.ssci.2007.07.005
   Web of Science ®Google Scholar
• PBS. (2017). Provisional summary results of 6th population and housing census 2017. Pakistan Bureau of Statistics.
   Google Scholar
• Quddus, M., Noland, R., & Chin, H. (2002). An analysis of motorcycle injury and vehicle damage severity using ordered probit models. Journal of Safety Research, 33(4), 445–462. https://doi.org/10.1016/s0022-4375(02)00051-8
   PubMed Web of Science ®Google Scholar
• Razi-Ardakani, H., Mahmoudzadeh, A., & Kermanshah, M. (2019). What factors results in having a severe crash? A closer look on distraction-related factors. Cogent Engineering, 6(1), 1708652. https://doi.org/10.1080/23311916.2019.1708652
   Web of Science ®Google Scholar
• Rescue1122. (2016). Consolidated Report of Road Traffic Emergencies. Punjab Emergency Service.
   Google Scholar
• Rifaat, S., Tay, R., & De Barros, A. (2012). Severity of motorcycle crashes in Calgary. Accident; Analysis and Prevention, 49, 44–49. https://doi.org/10.1016/j.aap.2011.02.025
   PubMed Web of Science ®Google Scholar
• Savolainen, P., & Mannering, F. (2007). Probabilistic models of motorcyclists’ injury severities in single-and multi-vehicle crashes. Accident; Analysis and Prevention, 39(5), 955–963. https://doi.org/10.1016/j.aap.2006.12.016
   PubMed Web of Science ®Google Scholar
• Schneider, W., & Savolainen, P. (2011). Comparison of severity of motorcyclist injury by crash types. Transportation Research Record: Journal of the Transportation Research Board, 2265(1), 70–80. https://doi.org/10.3141/2265-08
   Web of Science ®Google Scholar
• Se, C., Champahom, T., Jomnonkwao, S., & Ratanavaraha, V. (2022). Motorcyclist injury severity analysis: A comparison of Artificial Neural Networks and random parameter model with heterogeneity in means and variances. International Journal of Injury Control and Safety Promotion, 29(4), 500–515. https://doi.org/10.1080/17457300.2022.2081985
   PubMed Web of Science ®Google Scholar
• Seraneeprakarn, P., Huang, S., Shankar, V., Mannering, F., Venkataraman, N., & Milton, J. (2017). Occupant injury severities in hybrid-vehicle involved crashes: A random parameters approach with heterogeneity in means and variances. Analytic Methods in Accident Research, 15, 41–55. https://doi.org/10.1016/j.amar.2017.05.003
   Web of Science ®Google Scholar
• Shaheed, M., & Gkritza, K. (2014). A latent class analysis of single-vehicle motorcycle crash severity outcomes. Analytic Methods in Accident Research, 2, 30–38. https://doi.org/10.1016/j.amar.2014.03.002
   Google Scholar
• Shaheed, M., Gkritza, K., Zhang, W., & Hans, Z. (2013). A mixed logit analysis of two-vehicle crash severities involving a motorcycle. Accident; Analysis and Prevention, 61, 119–128. https://doi.org/10.1016/j.aap.2013.05.028
   PubMed Web of Science ®Google Scholar
• Shankar, V., & Mannering, F. (1996). An exploratory multinomial logit analysis of single-vehicle motorcycle accident severity. Journal of Safety Research, 27(3), 183–194. https://doi.org/10.1016/0022-4375(96)00010-2
   Web of Science ®Google Scholar
• Wang, C., Chen, F., Zhang, Y., & Cheng, J. (2022). Spatiotemporal instability analysis of injury severities in truck-involved and non-truck-involved crashes. Analytic Methods in Accident Research, 34, 100214. https://doi.org/10.1016/j.amar.2022.100214
   Web of Science ®Google Scholar
• Wang, C., Chen, F., Zhang, Y., Wang, S., Yu, B., & Cheng, J. (2022). Temporal stability of factors affecting injury severity in rear-end and non-rear-end crashes: A random parameter approach with heterogeneity in means and variances. Analytic Methods in Accident Research, 35, 100219. https://doi.org/10.1016/j.amar.2022.100219
   Web of Science ®Google Scholar
• Wang, C., Ijaz, M., Chen, F., Zhang, Y., Cheng, J., & Zahid, M. (2022). Evaluating gender differences in injury severities of non-helmet wearing motorcyclists: Accommodating temporal shifts and unobserved heterogeneity. Analytic Methods in Accident Research, 36, 100249. https://doi.org/10.1016/j.amar.2022.100249
   Web of Science ®Google Scholar
• Wang, Z., Lee, C., & Lin, P. (2014). Modeling injury severity of single-motorcycle crashes on curved roadway segments [Paper presentation]. In 93rd Annual Meeting of the Transportation Research Board, Washington, DC.
   Google Scholar
• Waseem, M., Ahmed, A., & Saeed, T. (2019). Factors affecting motorcyclists’ injury severities: an empirical assessment using random parameters logit model with heterogeneity in means and variances. Accident; Analysis and Prevention, 123, 12–19. https://doi.org/10.1016/j.aap.2018.10.022
   PubMed Web of Science ®Google Scholar
• Washington, S., Karlaftis, M., Mannering, F., & Anastasopoulos, P. (2020). Statistical and econometric methods for transportation data analysis (3rd ed.). CRC Press, Taylor and Francis Group.
   Google Scholar
• WHO (World Health Organization). (2009). Global Status Report on Road Safety 2009. ISBN: 978-92-4-156384-0.
   Google Scholar
• WHO (World Health Organization). (2018). Global status report on road safety 2018. ISBN:978-92-4-156568-4.
   Google Scholar
• Xin, C., Wang, Z., Lee, C., & Lin, P. (2017). Modeling safety effects of horizontal curve design on injury severity of single-motorcycle crashes with mixed-effects logistic model. Transportation Research Record: Journal of the Transportation Research Board, 2637(1), 38–46. https://doi.org/10.3141/2637-05
   Google Scholar
• Xu, P., Zhou, H., & Wong, S. (2021). On random-parameter count models for out-of-sample crash prediction: Accounting for the variances of random-parameter distributions. Accident; Analysis and Prevention, 159, 106237. https://doi.org/10.1016/j.aap.2021.106237
   PubMed Web of Science ®Google Scholar
• Yan, X., He, J., Wu, G., Zhang, C., Wang, C., & Ye, Y. (2022). Differences of overturned and hit-fixed-object crashes on rural roads accompanied by speeding driving: accommodating potential temporal shifts. Analytic Methods in Accident Research, 35, 100220. https://doi.org/10.1016/j.amar.2022.100220
   Web of Science ®Google Scholar
• Yan, X., He, J., Zhang, C., Liu, Z., Wang, C., & Qiao, B. (2021a). Temporal analysis of crash severities involving male and female drivers: A random parameters approach with heterogeneity in means and variances. Analytic Methods in Accident Research, 30, 100161. https://doi.org/10.1016/j.amar.2021.100161
   Web of Science ®Google Scholar
• Yan, X., He, J., Zhang, C., Liu, Z., Wang, C., & Qiao, B. (2021b). Spatiotemporal instability analysis considering unobserved heterogeneity of crash-injury severities in adverse weather. Analytic Methods in Accident Research, 32, 100182. https://doi.org/10.1016/j.amar.2021.100182
   Web of Science ®Google Scholar
• Yan, X., He, J., Wu, G., Zhang, C., Liu, Z., & Wang, C. (2021). Weekly variations and temporal instability of determinants influencing alcohol-impaired driving crashes: A random thresholds random parameters hierarchical ordered probit model. Analytic Methods in Accident Research, 32, 100189. https://doi.org/10.1016/j.amar.2021.100189
   Web of Science ®Google Scholar
• Zamani, A., Behnood, A., & Davoodi, S. (2021). Temporal stability of pedestrian injury severity in pedestrian-vehicle crashes: New insights from random parameter logit model with heterogeneity in means and variances. Analytic Methods in Accident Research, 32, 100184. https://doi.org/10.1016/j.amar.2021.100184
   Web of Science ®Google Scholar