Factors affecting the accident size of motorcycle-involved crashes: a structural equation modeling approach

References

• Ahmad, N., Ahmed, A., Wali, B., & Saeed, T. U. (2019). Exploring factors associated with crash severity on motorways in Pakistan. In Proceedings of the Institution of Civil Engineers-Transport (pp. 1–10). Thomas Telford Ltd. https://doi.org/10.1680/jtran.18.00032
   Google Scholar
• Ahmed, A., Saeed, T. U., Murillo-Hoyos, J., & Labi, S. (2017). Pavement repair marginal costs: Accounting for heterogeneity using random-parameters regression. Journal of Infrastructure Systems, 23(4), 04017012. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000367
   Web of Science ®Google Scholar
• Angel, A., & Hickman, M. (2008). Analysis of the factors affecting the severity of two-vehicle crashes. Ingeniería y Desarrollo 24, 176–194.
   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
• Chen, S., Saeed, T. U., Alinizzi, M., Lavrenz, S., & Labi, S. (2019). Safety sensitivity to roadway characteristics: A comparison across highway classes. Accident Analysis and Prevention, 123, 39–50. https://doi.org/10.1016/j.aap.2018.10.020
   PubMed Web of Science ®Google Scholar
• Chen, S., Saeed, T. U., & Labi, S. (2017). Impact of road-surface condition on rural highway safety: A multivariate random parameters negative binomial approach. Analytic Methods in Accident Research, 16, 75–89. https://doi.org/10.1016/j.amar.2017.09.001
   Web of Science ®Google Scholar
• Chung, Y., & Song, T.-J. (2018). Safety analysis of motorcycle crashes in Seoul metropolitan area, South Korea: An application of nonlinear optimal scaling methods. International Journal of Environmental Research and Public Health, 15(12), 2702. https://doi.org/10.3390/ijerph15122702
   PubMed Web of Science ®Google Scholar
• Cunto, F. J., & Ferreira, S. (2017). An analysis of the injury severity of motorcycle crashes in Brazil using mixed ordered response models. Journal of Transportation Safety & Security, 9(Supp1), 33–46. https://doi.org/10.1080/19439962.2016.1162891
   Google Scholar
• Duddu, V. R., Penmetsa, P., & Pulugurtha, S. S. (2018). Modeling and comparing injury severity of at-fault and not at-fault drivers in crashes. Accident Analysis and Prevention, 120, 55–63. https://doi.org/10.1016/j.aap.2018.07.036
   PubMed Web of Science ®Google Scholar
• Elyasi, M. R., Saffarzadeh, M., Broujerdian, A. M. (2018). Assessing the interrelations of traffic collisions' risk factors. In Proceedings of the Institution of Civil Engineers-Transport. Thomas Telford Ltd. https://doi.org/10.1680/jtran.16.00070.
   Google Scholar
• Eustace, D., & Dissanayake, S. (2016). Risk and Protective Factors Associated with Motorcycle Injury Severity in the United States. Nova Science Publishers.
   Google Scholar
• Geedipally, S. R., Turner, P. A., & Patil, S. (2011). Analysis of motorcycle crashes in Texas with multinomial logit model. Transportation Research Record: Journal of the Transportation Research Board, 2265(1), 62–69. https://doi.org/10.3141/2265-07
   Google Scholar
• Hassan, H. M., & Al-Faleh, H. (2013). Exploring the risk factors associated with the size and severity of roadway crashes in Riyadh. Journal of Safety Research, 47, 67–74. https://doi.org/10.1016/j.jsr.2013.09.002
   PubMed Web of Science ®Google Scholar
• Hefny, A. F., Barss, P., Eid, H. O., & Abu-Zidan, F. M. (2012). Motorcycle-related injuries in the United Arab Emirates. Accident; Analysis and Prevention, 49, 245–248. https://doi.org/10.1016/j.aap.2011.05.003
   PubMed Web of Science ®Google Scholar
• Jafari Nasab, E., Naseralavi, S., & Pourmoallem, N. (2015). Investigating the influence of vehicular ad-hoc networks on driver behaviours and traffic accidents. Travel Behaviour and Society, 2(3), 156–165. https://doi.org/10.1016/j.tbs.2015.01.001
   Web of Science ®Google Scholar
• Kaplan, S., & Prato, C. G. (2012). Risk factors associated with bus accident severity in the United States: A generalized ordered logit model. Journal of Safety Research, 43(3), 171–180. https://doi.org/10.1016/j.jsr.2012.05.003
   PubMed Web of Science ®Google Scholar
• Lee, J.-Y., Chung, J.-H., & Son, B. (2008). Analysis of traffic accident size for Korean highway using structural equation models. Accident; Analysis and Prevention, 40(6), 1955–1963. https://doi.org/10.1016/j.aap.2008.08.006
   PubMed Web of Science ®Google Scholar
• Rad, M., Martiniuk, A. L., Ansari-Moghaddam, A., Mohammadi, M., Rashedi, F., & Ghasemi, A. (2016). The pattern of road traffic crashes in South East Iran. Global Journal of Health Science, 8(9), 149. https://doi.org/10.5539/gjhs.v8n9p149
   Google Scholar
• Moghaddam, F. R., Afandizadeh, S., & Ziyadi, M. (2011). Prediction of accident severity using artificial neural networks. International Journal of Civil Engineering, 9(1), 41. http://ijce.iust.ac.ir/article-1-544-en.html
   Web of Science ®Google Scholar
• Nilsson, G. (2004). Traffic safety dimensions and the power model to describe the effect of speed on safety. Lund Institute of Technology and Society, Traffic Engineering.
   Google Scholar
• Pour-Rouholamin, M., Jalayer, M., & Zhou, H. (2017). Modelling single-vehicle, single-rider motorcycle crash injury severity: An ordinal logistic regression approach. International Journal of Urban Sciences, 21(3), 344–363. https://doi.org/10.1080/12265934.2017.1311801
   Web of Science ®Google Scholar
• Quddus, M. A., Noland, R. B., & Chin, H. C. (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
• Rezapour, M., Molan, A. M., & Ksaibati, K. (2020). Analyzing injury severity of motorcycle at-fault crashes using machine learning techniques, decision tree and logistic regression models. International Journal of Transportation Science and Technology, 9(2), 89–99. https://doi.org/10.1016/j.ijtst.2019.10.002
   Google Scholar
• Saeed, T. U. (2019). Road infrastructure readiness for autonomous vehicles. Purdue University Graduate School.
   Google Scholar
• Saeed, T. U., Hall, T., Baroud, H., & Volovski, M. J. (2019). Analyzing road crash frequencies with uncorrelated and correlated random-parameters count models: An empirical assessment of multilane highways. Analytic Methods in Accident Research, 23, 100101. https://doi.org/10.1016/j.amar.2019.100101
   Web of Science ®Google Scholar
• Saeed, T. U., Nateghi, R., Hall, T., & Waldorf, B. S. (2020). Statistical analysis of area‐wide alcohol‐related driving crashes: a spatial econometric approach. Geographical Analysis, 52(3), 394–417. https://doi.org/10.1111/gean.12216
   Web of Science ®Google Scholar
• Saeed, T. U., Qiao, Y., Chen, S., Gkritza, K., & Labi, S. (2017). Methodology for probabilistic modeling of highway bridge infrastructure condition: Accounting for improvement effectiveness and incorporating random effects. Journal of Infrastructure Systems, 23(4), 04017030. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000389
   Web of Science ®Google Scholar
• Salum, J. H., Kitali, A. E., Bwire, H., Sando, T., & Alluri, P. (2019). Severity of motorcycle crashes in Dar es Salaam. Traffic Injury Prevention, 20(2), 189–195. https://doi.org/10.1080/15389588.2018.1544706
   PubMed Web of Science ®Google Scholar
• Shaaban, K., Gharraie, I., Sacchi, E., & Kim, I. (2019). Severity analysis of red-light-running-related crashes using structural equation modeling. Journal of Transportation Safety & Security, 1–20. https://doi.org/10.1080/19439962.2019.1629137
   Google Scholar
• Tavakoli Kashani, A., Mohammadian, A., & Besharati, M. M. (2017). Explorative analysis of motorcyclists’ injury severity pattern at a national level in Iran. Archives of Trauma Research, 6(4), 87–93. https://doi.org/10.4103/atr.atr_38_17
   Web of Science ®Google Scholar
• Tiwari, G. (2013). Motorcycle mobility and traffic risk. International Journal of Injury Control and Safety Promotion, 20(2), 101–102. https://doi.org/10.1080/17457300.2013.800739
   PubMed Web of Science ®Google Scholar
• Ullman, J. B. (2006). Structural equation modeling: Reviewing the basics and moving forward. Journal of Personality Assessment, 87(1), 35–50. https://doi.org/10.1207/s15327752jpa8701_03
   PubMed Web of Science ®Google Scholar
• Vajari, M. A., Aghabayk, K., Sadeghian, M., & Shiwakoti, N. (2020). A multinomial logit model of motorcycle crash severity at Australian intersections. Journal of Safety Research, 73, 17–24. https://doi.org/10.1016/j.jsr.2020.02.008
   PubMed Web of Science ®Google Scholar
• Volovski, M., Murillo-Hoyos, J., Saeed, T. U., & Labi, S. (2017). Estimation of routine maintenance expenditures for highway pavement segments: accounting for heterogeneity using random-effects models. Journal of Transportation Engineering, Part A: Systems, 143(5), 04017006. https://doi.org/10.1061/JTEPBS.0000041
   Web of Science ®Google Scholar
• Wahab, L., & Jiang, H. (2019). A multinomial logit analysis of factors associated with severity of motorcycle crashes in Ghana. Traffic Injury Prevention, 20(5), 521–527. https://doi.org/10.1080/15389588.2019.1616699
   PubMed Web of Science ®Google Scholar
• Wang, J., & Wang, X. (2012). Structural equation modeling: Applications using Mplus. John Wiley & Sons.
   Google Scholar
• Waseem, M., Ahmed, A., & Saeed, T. U. (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
• Yadollahi, M., & Jamali, B. (2019). Severity and injury characteristics among matched hospitalized motorcycle drivers and their passengers. Chinese Journal of Traumatology = Zhonghua Chuang Shang za Zhi, 22(4), 223–227. https://doi.org/10.1016/j.cjtee.2018.08.007
   PubMed Web of Science ®Google Scholar
• Yamany, M. S., Saeed, T. U., Volovski, M., & Ahmed, A. (2020). Characterizing the performance of interstate flexible pavements using artificial neural networks and random parameters regression. Journal of Infrastructure Systems, 26(2), 04020010. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000542
   Web of Science ®Google Scholar
• Yuan, Y., Yang, M., Gan, Z., Wu, J., Xu, C., & Lei, D. (2019). Analysis of the risk factors affecting the size of fatal accidents involving trucks based on the structural equation model. Transportation Research Record: Journal of the Transportation Research Board, 2673(8), 112–124. https://doi.org/10.1177/0361198119841042
   Web of Science ®Google Scholar
• Zhou, M., & Chin, H. C. (2019). Factors affecting the injury severity of out-of-control single-vehicle crashes in Singapore. Accident; Analysis and Prevention, 124, 104–112. https://doi.org/10.1016/j.aap.2019.01.009
   PubMed Web of Science ®Google Scholar