What factors results in having a severe crash? a closer look on distraction-related factors

References

• Abdel-Aty, M. (2003). Analysis of driver injury severity levels at multiple locations using ordered probit models. Journal of Safety Research, 34, 597–30. doi:10.1016/j.jsr.2003.05.009
   PubMed Web of Science ®Google Scholar
• Abdel-Aty, M., & Abdelwahab, H. (2004). Modeling rear-end collisions including the role of driver’s visibility and light truck vehicles using a nested logit structure. Accident Analysis & Prevention, 36, 447–456. doi:10.1016/S0001-4575(03)00040-X
   PubMed Web of Science ®Google Scholar
• Abdel-Aty, M., Ekram, -A.-A., Huang, H., & Choi, K. (2011). A study on crashes related to visibility obstruction due to fog and smoke. Accident Analysis & Prevention, 43, 1730–1737. doi:10.1016/j.aap.2011.04.003
   PubMed Web of Science ®Google Scholar
• Abdel-Aty, M., & Keller, J. (2005). Exploring the overall and specific crash severity levels at signalized intersections. Accident Analysis & Prevention, 37, 417–425. doi:10.1016/j.aap.2004.11.002
   PubMed Web of Science ®Google Scholar
• Abhari, M., Tabibi, M., Nejad, F. M., Khansari, E. R., & Amini, E. (1888–1896). The effect of playing video advertisement inside a car on driver visual distraction. Civil Engineering Journal, 4, 2018.
   Google Scholar
• Ahangari, S., Jeihani, M., & Jarju, A.(2018). Drivers’ behavior analysis under rainy weather conditions using a driving simulator. Washington, DC: Transportation Research Board.
   Google Scholar
• Al-Ghamdi, A. S. (2002). Using logistic regression to estimate the influence of accident factors on accident severity. Accident Analysis & Prevention, 34, 729–741. doi:10.1016/S0001-4575(01)00073-2
   PubMed Web of Science ®Google Scholar
• Altwaijri, S., Quddus, M., & Bristow, A.(2011). Factors affecting the severity of traffic crashes in Riyadh City. 90th Annual Meeting, Washington, DC: Transportation Research Board. https://trid.trb.org/view/1092996
   Google Scholar
• Altwaijri, S., Quddus, M., & Bristow, A. (2012). Analysing the severity and frequency of traffic crashes in riyadh city using statistical models. International Journal of Transportation Science and Technology, 1, 351–364. doi:10.1260/2046-0430.1.4.351
   Google Scholar
• 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 & Prevention, 72, 95–104. doi:10.1016/j.aap.2014.06.015
   PubMed Web of Science ®Google Scholar
• Amarasingha, N., & Dissanayake, S. (2014). Gender differences of young drivers on injury severity outcome of highway crashes. Journal of Safety Research, 49, 113.e1–120. doi:10.1016/j.jsr.2014.03.004
   PubMed Web of Science ®Google Scholar
• Anastasopoulos, P. C., & Mannering, F. L. (2011). An empirical assessment of fixed and random parameter logit models using crash- and non-crash-specific injury data. Accident Analysis & Prevention, 43, 1140–1147. doi:10.1016/j.aap.2010.12.024
   PubMed Web of Science ®Google Scholar
• Arvin, R., Kamrani, M., & Khattak, A. J. (2019). The role of pre-crash driving instability in contributing to crash intensity using naturalistic driving data. Accident Analysis & Prevention, 132, 105226. doi:10.1016/j.aap.2019.07.002
   PubMed Web of Science ®Google Scholar
• Azimi, G., Asgari, H., Rahimi, A., & Jin, X. (2019). Investigation of heterogeneity in severity analysis for large truck crashes. Washington, DC: Transportation Research Board. https://trid.trb.org/view/1658006
   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
• Aziz, H. M. A., Ukkusuri, S. V., & Hasan, S. (2013). Exploring the determinants of pedestrian–Vehicle crash severity in New York City. Accident Analysis & Prevention, 50, 1298–1309. doi:10.1016/j.aap.2012.09.034
   PubMed Web of Science ®Google Scholar
• Azizi, L., & Sheikholeslami, A. (2013). Safety effect of U-Turn Conversions in Tehran: Empirical bayes observational before-and-after study and crash prediction models. Journal of Transportation Engineering, 139, 101–108. doi:10.1061/(ASCE)TE.1943-5436.0000469
   Google Scholar
• Azizi, L., Sheikholeslami, A., & Khalili, F. B. (2012). Safety analysis of unconventional U-Turn using neural network and crash prediction model. 10th International Congress on Advances in Civil Engineering (pp. 17–19). Ankara, Turkey.
   Google Scholar
• Backer-Grøndahl, A., & Sagberg, F. (2011). Driving and telephoning: Relative accident risk when using hand-held and hands-free mobile phones. Safety Science, 49, 324–330. doi:10.1016/j.ssci.2010.09.009
   Web of Science ®Google Scholar
• Beanland, V., Fitzharris, M., Young, K. L., & Lenné, M. G. (2013). Driver inattention and driver distraction in serious casualty crashes: Data from the Australian national crash in-depth study. Accident Analysis & Prevention, 54, 99–107. doi:10.1016/j.aap.2012.12.043
   PubMed Web of Science ®Google Scholar
• Bedard, M., Guyatt, G. H., Stones, M. J., & Hirdes, J. P. (2002). The independent contribution of driver, crash, and vehicle characteristics to driver fatalities. Accident Analysis & Prevention, 34, 717–727. doi:10.1016/S0001-4575(01)00072-0
   PubMed Web of Science ®Google Scholar
• Behnood, A., & Mannering, F. L. (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. doi:10.1016/j.amar.2015.08.001
   Web of Science ®Google Scholar
• Behnood, A., & Mannering, F. L. (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. doi:10.1016/j.amar.2016.07.002
   Web of Science ®Google Scholar
• Behnood, A., Roshandeh, A. M., & Mannering, F. L. (2014). Latent class analysis of the effects of age, gender, and alcohol consumption on driver-injury severities. Analytic Methods in Accident Research 3–4, 56–91. doi:10.1016/j.amar.2014.10.001
   Google Scholar
• Behnood, A., Roshandeh, A. M., & Modiri Gharehveran, M. (2015). The effects of drivers’ behavior on driver-injury severities in Iran: An application of the mixed-logit model. Science Iranica, 23 (6), 2429–2440.
   Web of Science ®Google Scholar
• Boufous, S., de Rome, L., Senserrick, T., & Ivers, R. (2012). Risk factors for severe injury in cyclists involved in traffic crashes in Victoria, Australia. Accident Analysis & Prevention, 49, 404–409. doi:10.1016/j.aap.2012.03.011
   PubMed Web of Science ®Google Scholar
• Brant, R. (1990). Assessing proportionality in the proportional odds model for ordinal logistic regression. Biometrics, 46, 1171–1178. doi:10.2307/2532457
   PubMed Web of Science ®Google Scholar
• Carson, J., & Mannering, F. (2001). The effect of ice warning signs on ice-accident frequencies and severities. Accident Analysis & Prevention, 33, 99–109. doi:10.1016/S0001-4575(00)00020-8
   PubMed Web of Science ®Google Scholar
• Castro, M., Paleti, R., & Bhat, C. R. (2013). A spatial generalized ordered response model to examine highway crash injury severity. Accident Analysis & Prevention, 49, 188–203. doi:10.1016/j.aap.2012.12.009
   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, L.-Y., & Mannering, F. (1998). Predicting vehicle occupancies from accident data: An accident severity approach. Transportation Research Record: Journal of the Transportation Research Board, 1635, 93–104. doi:10.3141/1635-13
   Google Scholar
• Chang, L.-Y., & Mannering, F. (1999). Analysis of injury severity and vehicle occupancy in truck- and non-truck-involved accidents. Accident Analysis & Prevention, 31, 579–592. doi:10.1016/S0001-4575(99)00014-7
   PubMed Web of Science ®Google Scholar
• Chen, C., Zhang, G., Tarefder, R., Ma, J., Wei, H., & Guan, H. (2015). A multinomial logit model-Bayesian network hybrid approach for driver injury severity analyses in rear-end crashes. Accident Analysis & Prevention, 80, 76–88. doi:10.1016/j.aap.2015.03.036
   PubMed Web of Science ®Google Scholar
• Chiou, Y. C., Hwang, C. C., Chang, C. C., Fu, C., Chiou, Y.-C., Hwang, -C.-C., … Fu, C. (2013). Modeling two-vehicle crash severity by a bivariate generalized ordered probit approach. Accident Analysis & Prevention, 51, 175–184. doi:10.1016/j.aap.2012.11.008
   PubMed Web of Science ®Google Scholar
• Chiou, Y. C., Lan, L. W., & Chen, W. P. (2013). A two-stage mining framework to explore key risk conditions on one-vehicle crash severity. Accident Analysis & Prevention, 50, 405–415. doi:10.1016/j.aap.2012.05.017
   PubMed Web of Science ®Google Scholar
• Christoforou, Z., Cohen, S., & Karlaftis, M. G. (2010). Vehicle occupant injury severity on highways: An empirical investigation. Accident Analysis & Prevention, 42, 1606–1620. doi:10.1016/j.aap.2010.03.019
   PubMed Web of Science ®Google Scholar
• Chung, Y., Song, T.-J., & Yoon, B.-J. (2014). Injury severity in delivery-motorcycle to vehicle crashes in the Seoul metropolitan area. Accident Analysis & Prevention, 62, 79–86. doi:10.1016/j.aap.2013.08.024
   PubMed Web of Science ®Google Scholar
• Clogg, C., & Shihadeh, E. S. (1994). Statistical models for ordinal variables (Advanced quantitative techniques in the social sciences series, Vol. 4).
   Google Scholar
• Curry, A. E., Hafetz, J., Kallan, M. J., Winston, F. K., & Durbin, D. R. (2011). Prevalence of teen driver errors leading to serious motor vehicle crashes. Accident Analysis & Prevention, 43, 1285–1290. doi:10.1016/j.aap.2010.10.019
   PubMed Web of Science ®Google Scholar
• Das, A., Pande, A., Abdel-Aty, M., & Santos, J. (2008). Characteristics of urban arterial crashes relative to proximity to intersections and injury severity. Transportation Research Record: Journal of the Transportation Research Board, 2083, 137–144. doi:10.3141/2083-16
   Web of Science ®Google Scholar
• de Lapparent, M. (2008). Willingness to use safety belt and levels of injury in car accidents. Accident Analysis & Prevention, 40, 1023–1032. doi:10.1016/j.aap.2007.11.005
   PubMed Web of Science ®Google Scholar
• de Waard, D., Schepers, P., Ormel, W., & Brookhuis, K. (2010). Mobile phone use while cycling: Incidence and effects on behaviour and safety. Ergonomics, 53, 30–42. doi:10.1080/00140130903381180
   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. doi:10.1016/j.jsr.2014.12.001
   PubMed Web of Science ®Google Scholar
• Eluru, N., Paleti, R., Pendyala, R. M., & Bhat, C. R. (2010). Modeling multiple vehicle occupant injury severity: Acopula-based multivariate approach. Transportation Research Record Journal of the Transportation Research Board, 8164, 1–24. doi:10.3141/2165-01
   Google Scholar
• Feng, S., Li, Z., Ci, Y., & Zhang, G. (2016). Risk factors affecting fatal bus accident severity: Their impact on different types of bus drivers. Accident Analysis & Prevention, 86, 29–39. doi:10.1016/j.aap.2015.09.025
   PubMed Web of Science ®Google Scholar
• Fowles, R., Loeb, P. D., & Clarke, W. A. (2010). The cell phone effect on motor vehicle fatality rates: A Bayesian and classical econometric evaluation. Transportation Research Part E: Logistics and Transportation Review, 46, 1140–1147. doi:10.1016/j.tre.2010.02.001
   Web of Science ®Google Scholar
• Gårder, P. (2006). Segment characteristics and severity of head-on crashes on two-lane rural highways in Maine. Accident Analysis & Prevention, 38, 652–661. doi:10.1016/j.aap.2005.12.009
   PubMed Web of Science ®Google Scholar
• Gray, R. C., Quddus, M. A., & Evans, A. (2008). Injury severity analysis of accidents involving young male drivers in Great Britain. Journal of Safety Research, 39, 483–495. doi:10.1016/j.jsr.2008.07.003
   PubMed Web of Science ®Google Scholar
• Greene, W. H., & Hensher, D. A. (2010). Modeling ordered choices: A primer. Cambridge University Press.
   Google Scholar
• Haleem, K., & Abdel-Aty, M. (2010). Examining traffic crash injury severity at unsignalized intersections. Journal of Safety Research, 41, 347–357. doi:10.1016/j.jsr.2010.04.006
   PubMed Web of Science ®Google Scholar
• Haleem, K., & Gan, A. (2011). Identifying traditional and nontraditional predictors of crash injury severity on major urban roadways. Traffic Injury Prevention, 12, 223–234. doi:10.1080/15389588.2011.557110
   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
• Hanley, P. F., & Sikka, N. (2012). Bias caused by self-reporting distraction and its impact on crash estimates. Accident Analysis & Prevention, 49, 360–365. doi:10.1016/j.aap.2012.02.008
   PubMed Web of Science ®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. doi:10.1016/j.jsr.2013.09.002
   PubMed Web of Science ®Google Scholar
• Holdridge, J. M., Shankar, V. N., & Ulfarsson, G. F. (2005). The crash severity impacts of fixed roadside objects. Journal of Safety Research, 36, 139–147. doi:10.1016/j.jsr.2004.12.005
   PubMed Web of Science ®Google Scholar
• Hosseinpour, M., Yahaya, A. S., & Sadullah, A. F. (2014). Exploring the effects of roadway characteristics on the frequency and severity of head-on crashes: Case studies from Malaysian federal roads. Accident Analysis & Prevention, 62, 209–222. doi:10.1016/j.aap.2013.10.001
   PubMed Web of Science ®Google Scholar
• Hu, W., & Donnell, E. T. (2011a). Severity models of cross-median and rollover crashes on rural divided highways in Pennsylvania. Journal of Safety Research, 42, 375–382. doi:10.1016/j.jsr.2011.07.004
   PubMed Web of Science ®Google Scholar
• Hu, W., & Donnell, E. T. (2011b). Median barrier crash severity: Some new insights. Accident Analysis & Prevention, 42, 1697–1704. doi:10.1016/j.aap.2010.04.009
   Web of Science ®Google Scholar
• Huang, H., Chin, H. C., & Haque, M. M. (2008). Severity of driver injury and vehicle damage in traffic crashes at intersections: A Bayesian hierarchical analysis. Accident Analysis & Prevention, 40, 45–54. doi:10.1016/j.aap.2007.04.002
   PubMed Web of Science ®Google Scholar
• Islam, S., Jones, S. L., & Dye, D. (2014). Comprehensive analysis of single- and multi-vehicle large truck at-fault crashes on rural and urban roadways in Alabama. Accident Analysis & Prevention, 67, 148–158. doi:10.1016/j.aap.2014.02.014
   PubMed Web of Science ®Google Scholar
• Islam, S., & Mannering, F. (2006). Driver aging and its effect on male and female single-vehicle accident injuries: Some additional evidence. Journal of Safety Research, 37, 267–276. doi:10.1016/j.jsr.2006.04.003
   PubMed Web of Science ®Google Scholar
• Ismael, K. S., & Razzaq, N. A. (2017). Traffic accidents analysis on dry and wet road bends surfaces in greater Manchester-UK. Kurdistan Journal of Applied Research, 2, 284–291. doi:10.24017/science.2017.3.51
   Google Scholar
• Jiang, X., Huang, B., Zaretzki, R. L., Richards, S., Yan, X., & Zhang, H. (2013). Investigating the influence of curbs on single-vehicle crash injury severity utilizing zero-inflated ordered probit models. Accident Analysis & Prevention, 57, 55–66. doi:10.1016/j.aap.2013.03.018
   PubMed Web of Science ®Google Scholar
• Jung, S., Jang, K., Yoon, Y., & Kang, S. (2014). Contributing factors to vehicle to vehicle crash frequency and severity under rainfall. Journal of Safety Research, 50, 1–10. doi:10.1016/j.jsr.2014.01.001
   PubMed Web of Science ®Google Scholar
• Jung, S., Qin, X., & Noyce, D. A. (2010). Rainfall effect on single-vehicle crash severities using polychotomous response models. Accident Analysis & Prevention, 42, 213–224. doi:10.1016/j.aap.2009.07.020
   PubMed Web of Science ®Google Scholar
• Kamrani, M., Arvin, R., & Khattak, A. J. (2019). The role of aggressive driving and speeding in road safety: Insights from SHRP2 naturalistic driving study data. Washington, DC: Transportation Research Board.
   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, 171–180. doi:10.1016/j.jsr.2012.05.003
   PubMed Web of Science ®Google Scholar
• Khattak, A. (2001). Injury severity in multivehicle rear-end crashes. Transportation Research Record: Journal of the Transportation Research Board, 1746, 59–68. doi:10.3141/1746-08
   Web of Science ®Google Scholar
• Khattak, A., Kantor, P., & Council, F. (1998). Role of adverse weather in key crash types on limited-access: Roadways implications for advanced weather systems. Transportation Research Record: Journal of the Transportation Research Board, 10–19. doi:10.3141/1621-02
   Web of Science ®Google Scholar
• Khattak, A., & Rocha, M. (2003). Are SUVs” supremely unsafe vehicles”?: Analysis of rollovers and injuries with sport utility vehicles. Transportation Research Record: Journal of the Transportation Research Board, 1840, 167–177. doi:10.3141/1840-19
   Google Scholar
• Kim, J.-K., Ulfarsson, G. F., Kim, S., & Shankar, V. N. (2013). Driver-injury severity in single-vehicle crashes in California: A mixed logit analysis of heterogeneity due to age and gender. Accident Analysis & Prevention, 50, 1073–1081. doi:10.1016/j.aap.2012.08.011
   PubMed Web of Science ®Google Scholar
• Kockelman, K. M., & Kweon, Y.-J. (2002). Driver injury severity: An application of ordered probit models. Accident Analysis & Prevention, 34, 313–321. doi:10.1016/S0001-4575(01)00028-8
   PubMed Web of Science ®Google Scholar
• Kononen, D. W., Flannagan, C. A. C., & Wang, S. C. (2011). Identification and validation of a logistic regression model for predicting serious injuries associated with motor vehicle crashes. Accident Analysis & Prevention, 43, 112–122. doi:10.1016/j.aap.2010.07.018
   PubMed Web of Science ®Google Scholar
• Kutner, M. H., Nachtsheim, C. J., Neter, J., & Li, W.  (2004). Applied linear regression models. Boston: McGraw-Hill/Irwin.
   Google Scholar
• Lansdown, T. C., Brook-Carter, N., & Kersloot, T. (2004). Distraction from multiple in-vehicle secondary tasks: Vehicle performance and mental workload implications. Ergonomics, 47, 91–104. doi:10.1080/00140130310001629775
   PubMed Web of Science ®Google Scholar
• Lee, C., & Abdel-Aty, M. (2008). Presence of passengers: Does it increase or reduce driver’s crash potential? Accident Analysis & Prevention, 40, 1703–1712. doi:10.1016/j.aap.2008.06.006
   PubMed Web of Science ®Google Scholar
• Lee, C., & Li, X. (2014). Analysis of injury severity of drivers involved in single- and two-vehicle crashes on highways in Ontario. Accident Analysis & Prevention, 71, 286–295. doi:10.1016/j.aap.2014.06.008
   PubMed Web of Science ®Google Scholar
• Lee, J., & Mannering, F. (2002). Impact of roadside features on the frequency and severity of run-off-roadway accidents: An empirical analysis. Accident Analysis & Prevention, 34, 149–161. doi:10.1016/S0001-4575(01)00009-4
   PubMed Web of Science ®Google Scholar
• Lemp, J. D., Kockelman, K. M., & Unnikrishnan, A. (2011). Analysis of large truck crash severity using heteroskedastic ordered probit models. Accident Analysis & Prevention, 43, 370–380. doi:10.1016/j.aap.2010.09.006
   PubMed Web of Science ®Google Scholar
• Li, Z., Liu, P., Wang, W., & Xu, C. (2012). Using support vector machine models for crash injury severity analysis. Accident Analysis & Prevention, 45, 478–486. doi:10.1016/j.aap.2011.08.016
   PubMed Web of Science ®Google Scholar
• Liu, Z., & Donmez, B. (2011). Effects of distractions on injury severity in police-involved crashes. Transportation Research Record (pp. 1–13). Washington, DC.
   Google Scholar
• Loeb, P. D., Clarke, W. A., & Anderson, R. (2009). The impact of cell phones on motor vehicle fatalities. Applied Economics, 41, 2905–2914. doi:10.1080/00036840701858133
   Web of Science ®Google Scholar
• Ma, L., Wang, G., Yan, X., & Weng, J. (2016). A hybrid finite mixture model for exploring heterogeneous ordering patterns of driver injury severity. Accident Analysis & Prevention, 89, 62–73. doi:10.1016/j.aap.2016.01.004
   PubMed Web of Science ®Google Scholar
• Mahmoudzadeh, A., Razi-Ardakani, H., & Kermanshah, M. (2019). Studying crash avoidance maneuvers prior to an impact considering different types of driver’s distractions. Transportation Research Procedia, 37, 203–210. doi:10.1016/J.TRPRO.2018.12.184
   Google Scholar
• Malyshkina, N., & Mannering, F. (2008). Effect of increases in speed limits on severities of injuries in accidents. Transportation Research Record: Journal of the Transportation Research Board, 2083, 122–127. doi:10.3141/2083-14
   Web of Science ®Google Scholar
• Malyshkina, N. V., & Mannering, F. L. (2010). Empirical assessment of the impact of highway design exceptions on the frequency and severity of vehicle accidents. Accident Analysis & Prevention, 42, 131–139. doi:10.1016/j.aap.2009.07.013
   PubMed Web of Science ®Google Scholar
• Manner, H., & Wünsch-Ziegler, L. (2013). Analyzing the severity of accidents on the German Autobahn. Accident Analysis & Prevention, 57, 40–48. doi:10.1016/j.aap.2013.03.022
   PubMed Web of Science ®Google Scholar
• Mansourkhaki, A., Karimpour, A., & Sadoghi Yazdi, H. (2016). Non-stationary concept of accident prediction. Proceedings of the Institution of Civil Engineers (pp. 140–151), Thomas Telford Ltd. doi:10.1142/S2424835516400063
   Google Scholar
• Mansourkhaki, A., Karimpour, A., & Yazdi, H. S. (1912–1918). Introducing prior knowledge for a hybrid accident prediction model. KSCE Journal of Civil Engineering, 21, 2017.
   Google Scholar
• McEvoy, S. P., Stevenson, M., McCartt, A. T., Woodward, M., Haworth, C., Palamara, P., & Cercarelli, R. (2005). Role of mobile phones in motor vehicle crashes resulting in hospital attendance: A case-crossover study. Bmj, 331, 428. doi:10.1136/bmj.38537.397512.55
   PubMed Web of Science ®Google Scholar
• McEvoy, S. P., Stevenson, M. R., & Woodward, M. (2006). Phone use and crashes while driving: A representative survey of drivers in two Australian states. Medical Journal of Australia, 185, 630–634. doi:10.5694/mja2.2006.185.issue-11-12
   PubMed Web of Science ®Google Scholar
• Mergia, W. Y., Eustace, D., Chimba, D., & Qumsiyeh, M. (2013). Exploring factors contributing to injury severity at freeway merging and diverging locations in Ohio. Accident Analysis & Prevention, 55, 202–210. doi:10.1016/j.aap.2013.03.008
   PubMed Web of Science ®Google Scholar
• Michalaki, P., Quddus, M. A., Pitfield, D., & Huetson, A. (2015). Exploring the factors affecting motorway accident severity in England using the generalised ordered logistic regression model. Journal of Safety Research, 55, 89–97. doi:10.1016/j.jsr.2015.09.004
   PubMed Web of Science ®Google Scholar
• Milton, J. C., Shankar, V. N., & Mannering, F. L. (2008). Highway accident severities and the mixed logit model: An exploratory empirical analysis. Accident Analysis & Prevention, 40, 260–266. doi:10.1016/j.aap.2007.06.006
   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
• Mokhtarimousavi, S. (2019). A time of day analysis of pedestrian-involved crashes in California: Investigation of injury severity, a logistic regression and machine learning approach using HSIS data. Institute of Transportation Engineers ITE Journal, 89, 25–33.
   Web of Science ®Google Scholar
• Mokhtarimousavi, S., Anderson, J. C., Azizinamini, A., & Hadi, M. (2019). Improved support vector machine models for work zone crash injury severity prediction and analysis. Transportation Research Record, 2673, 680–692. doi:10.1177/0361198119845899
   Web of Science ®Google Scholar
• Mooradian, J., Ivan, J. N., Ravishanker, N., & Hu, S. (2013). Analysis of driver and passenger crash injury severity using partial proportional odds models. Accident Analysis & Prevention, 58, 53–58. doi:10.1016/j.aap.2013.04.022
   PubMed Web of Science ®Google Scholar
• Moore, D. N., Schneider, W. H., IV, 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 & Prevention, 43, 621–630. doi:10.1016/j.aap.2010.09.015
   PubMed Web of Science ®Google Scholar
• Morgan, A., & Mannering, F. L. (1852–1863). The effects of road-surface conditions, age, and gender on driver-injury severities. Accident Analysis & Prevention, 43, 2011.
   Google Scholar
• Motamedi, S., Hasheminejad, M., & Choe, P. (2015). Driving safety considered user interface of a smartphone: An experimental comparison, in: International Conference on Cross-Cultural Design (pp. 150–160), Springer. doi:10.1177/1753193414524689
   Google Scholar
• National Highway Traffic Safety Administration (NHTSA). (2017). Traffic safety facts, distracted driving in fatal crashes.
   Google Scholar
• Neyens, D. M., & Boyle, L. N. (2007). The effect of distractions on the crash types of teenage drivers. Accident Analysis & Prevention, 39, 206–212. doi:10.1016/j.aap.2006.07.004
   PubMed Web of Science ®Google Scholar
• Neyens, D. M., & Boyle, L. N. (2008). The influence of driver distraction on the severity of injuries sustained by teenage drivers and their passengers. Accident Analysis & Prevention, 40, 254–259. doi:10.1016/j.aap.2007.06.005
   PubMed Web of Science ®Google Scholar
• NHTSA. (2008). Driver Electronic Device Use.184, DOT HS 811.
   Google Scholar
• N.H.T.S. NHTSA. (2015). Traffic safety facts: Research note. Distracted driving 2013 (Report No. DOT HS 812 132).
   Google Scholar
• NHTSA’s National Center for Statistics and Analysis. (2015). DOT HS 812 326 Traffic Safety Facts, Research Note Driver Electronic Device Use in 2015, 2013, 1–8.
   Google Scholar
• O’donnell, C. J., & Connor, D. H. (1996). Predicting the severity of motor vehicle accident injuries using models of ordered multiple choice. Accident Analysis & Prevention, 28, 739–753. doi:10.1016/S0001-4575(96)00050-4
   PubMed Web of Science ®Google Scholar
• Obeng, K. (2011). Gender differences in injury severity risks in crashes at signalized intersections. Accident Analysis & Prevention, 43, 1521–1531. doi:10.1016/j.aap.2011.03.004
   PubMed Web of Science ®Google Scholar
• Oh, J. T. (2006). Development of severity models for vehicle accident injuries for signalized intersections in rural areas. KSCE Journal of Civil Engineering, 10, 219–225. doi:10.1007/BF02824064
   Google Scholar
• Paleti, R., Eluru, N., & Bhat, C. R. (2010). Examining the influence of aggressive driving behavior on driver injury severity in traffic crashes. Accident Analysis & Prevention, 42, 1839–1854. doi:10.1016/j.aap.2010.05.005
   PubMed Web of Science ®Google Scholar
• Parsa, A. B., Chauhan, R. S., Taghipour, H., & Derrible, S. (2019). Applying deep learning to detect traffic accidents in real time using spatiotemporal sequential data, ArXiv Prepr. ArXiv1912.06991 [Stat.ML].
   Google Scholar
• Parsa, A. B., Movahedi, A., Taghipour, H., Derrible, S., & Mohammadian, A. K. (2020). Toward Safer Highways, Application of XGBoost and SHAP for Real-Time Accident Detection and Feature Analysis. Accident Analysis & Prevention, 136, 105405. doi:10.1016/j.aap.2019.105405
   Google Scholar
• Parsa, A. B., Taghipour, H., Derrible, S., & Mohammadian, A. (2019). Real-time accident detection: Coping with imbalanced data. Accident Analysis & Prevention, 129, 202–210. doi:10.1016/j.aap.2019.05.014
   PubMed Web of Science ®Google Scholar
• Patil, S., Reddy, S., & Reddy, S. (2012). Analysis of crash severities using nested logit model-accounting for the underreporting of crash data. Accident Analysis & Prevention, 45, 646–653. doi:10.1016/j.aap.2011.09.034
   PubMed Web of Science ®Google Scholar
• Peek-Asa, C., Britton, C., Young, T., Pawlovich, M., & Falb, S. (2010). Teenage driver crash incidence and factors influencing crash injury by rurality. Journal of Safety Research, 41, 487–492. doi:10.1016/j.jsr.2010.10.002
   PubMed Web of Science ®Google Scholar
• Pei, X., Wong, S. C., & Sze, N. N. (2012). The roles of exposure and speed in road safety analysis. Accident Analysis & Prevention, 48, 464–471. doi:10.1016/j.aap.2012.03.005
   PubMed Web of Science ®Google Scholar
• Pour-Rouholamin, M., & Zhou, H. (2016). Analysis of driver injury severity in wrong-way driving crashes on controlled-access highways. Accident Analysis & Prevention, 94, 80–88. doi:10.1016/j.aap.2016.05.022
   PubMed Web of Science ®Google Scholar
• Pratt, M., & Bonneson, J. (2008). Assessing curve severity and design consistency using energy- and friction-based measures. Transportation Research Record: Journal of the Transportation Research Board, 2075, 8–15. doi:10.3141/2075-02
   Google Scholar
• Quddus, M. A., Wang, C., & Ison, S. G. (2009). Road traffic congestion and crash severity: Econometric analysis using ordered response models. Journal of Transportation Engineering, 136, 424–435. doi:10.1061/(ASCE)TE.1943-5436.0000044
   Web of Science ®Google Scholar
• Rana, T., Sikder, S., & Pinjari, A. (2010). Copula-based method for addressing endogeneity in models of severity of traffic crash injuries: Application to two-vehicle crashes. Transportation Research Record: Journal of the Transportation Research Board, 2147, 75–87. doi:10.3141/2147-10
   Web of Science ®Google Scholar
• Razi-Ardakani, H., Kermanshah, M., & Mahmoudzadeh, A. (2017). Crash type analysis using nested logit model: Special focus on distraction-related factors. Transportation Research Board, Washington D.C, No. 17–06836.
   Google Scholar
• Razi-Ardakani, H., Mahmoudzadeh, A., & Kermanshah, M. (2018). A nested logit analysis of the influence of distraction on types of vehicle crashes. European Transport Research Review, 10, 44. doi:10.1186/s12544-018-0316-6
   Web of Science ®Google Scholar
• Regan, M. (2006). Preventing traffic accidents by mobile phone users. Medical Journal of Australia, 185, 628–629. doi:10.5694/mja2.2006.185.issue-11-12
   PubMed Web of Science ®Google Scholar
• Reimer, B., Mehler, B., Coughlin, J. F., Roy, N., & Dusek, J. A. (2011). The impact of a naturalistic hands-free cellular phone task on heart rate and simulated driving performance in two age groups. Transportation Research Part F: Traffic Psychology and Behaviour, 14, 13–25. doi:10.1016/j.trf.2010.09.002
   Web of Science ®Google Scholar
• Renski, H., Khattak, A., & Council, F. (1999). Effect of speed limit increases on crash injury severity: Analysis of single-vehicle crashes on North Carolina interstate highways. Transportation Research Record: Journal of the Transportation Research Board, 1665, 100–108. doi:10.3141/1665-14
   Google Scholar
• Rifaat, S., & Tay, R. (2009). Effects of street patterns on injury risks in two-vehicle crashes. Transportation Research Record: Journal of the Transportation Research Board, 2102, 61–67. doi:10.3141/2102-08
   Web of Science ®Google Scholar
• Rifaat, S. M., Tay, R., & De Barros, A. (2012). Severity of motorcycle crashes in Calgary. Accident Analysis & Prevention, 49, 44–49. doi:10.1016/j.aap.2011.02.025
   PubMed Web of Science ®Google Scholar
• Rifaat, S. M., Tay, R., & De Barros, A. (2013). Effect of street pattern on the severity of crashes involving vulnerable road users. Accident Analysis & Prevention, 57, 276–283. doi:10.1016/j.aap.2010.08.024
   Google Scholar
• Roque, C., Moura, F., & Cardoso, J. L. (2015). Detecting unforgiving roadside contributors through the severity analysis of ran-off-road crashes. Accident Analysis & Prevention, 80, 262–273. doi:10.1016/j.aap.2015.02.012
   PubMed Web of Science ®Google Scholar
• Sasidharan, L., & Menéndez, M. (2014). Partial proportional odds model—An alternate choice for analyzing pedestrian crash injury severities. Accident Analysis & Prevention, 72, 330–340. doi:10.1016/j.aap.2014.07.025
   PubMed Web of Science ®Google Scholar
• Savolainen, P., & Ghosh, I. (2008). Examination of factors affecting driver injury severity in Michigan’s Single-Vehicle—Deer Crashes. Transportation Research Record: Journal of the Transportation Research Board, 2078, 17–25. doi:10.3141/2078-03
   Web of Science ®Google Scholar
• Savolainen, P., & Mannering, F. (2007). Probabilistic models of motorcyclists’ injury severities in single- and multi-vehicle crashes. Accident Analysis & Prevention, 39, 955–963. doi:10.1016/j.aap.2006.12.016
   PubMed Web of Science ®Google Scholar
• Savolainen, P. T., Mannering, F. L., Lord, D., & Quddus, M. A. (2011). The statistical analysis of highway crash-injury severities: A review and assessment of methodological alternatives. Accident Analysis & Prevention, 43, 1666–1676. doi:10.1016/j.aap.2011.03.025
   PubMed Web of Science ®Google Scholar
• Schneider, W., IV, Savolainen, P., & Zimmerman, K. (2009). Driver injury severity resulting from single-vehicle crashes along horizontal curves on rural two-lane highways. Transportation Research Record: Journal of the Transportation Research Board, 2102, 85–92. doi:10.3141/2102-11
   Web of Science ®Google Scholar
• Shaheed, M. S. B., Gkritza, K., Zhang, W., & Hans, Z. (2013). A mixed logit analysis of two-vehicle crash severities involving a motorcycle. Accident Analysis & Prevention, 61, 119–128. doi: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, 183–194. doi:10.1016/0022-4375(96)00010-2
   Web of Science ®Google Scholar
• Shankar, V., Mannering, F., & Barfield, W. (1996). Statistical analysis of accident severity on rural freeways. Accident Analysis & Prevention, 28, 391–401. doi:10.1016/0001-4575(96)00009-7
   PubMed Web of Science ®Google Scholar
• Shell, D. F., Newman, I. M., Córdova-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
• Shirani, N., Doustmohammadi, M., Haleem, K., & Anderson, M. (2018). Safety investigation of nonmotorized crashes in the City of Huntsville, Alabama, using count regression models. Washington, DC: Transportation Research Board.
   Google Scholar
• Siskind, V., Steinhardt, D., Sheehan, M., O’Connor, T., & Hanks, H. (2011). Risk factors for fatal crashes in rural Australia. Accident Analysis & Prevention, 43, 1082–1088. doi:10.1016/j.aap.2010.12.016
   PubMed Web of Science ®Google Scholar
• Strayer, D. L., & Drews, F. A. (2004). Profiles in driver distraction: Effects of cell phone conversations on younger and older drivers. Human Factors: the Journal of the Human Factors and Ergonomics Society, 46, 640–649. doi:10.1518/hfes.46.4.640.56806
   PubMed Web of Science ®Google Scholar
• Tay, R. (2016). Comparison of the binary logistic and skewed logistic (Scobit) models of injury severity in motor vehicle collisions. Accident Analysis & Prevention, 88, 52–55. doi:10.1016/j.aap.2015.12.009
   PubMed Web of Science ®Google Scholar
• Ulak, M. B., Ozguven, E., Karabag, H. H., Ghorbanzadeh, M., Moses, R., & Dulebenets, M. (2019). Development of safety performance functions for Restricted Crossing U-Turn (RCUT) intersections. Journal of Transportation Engineering Part A: Systems. doi: 10.1061/JTEPBS.0000346
   Web of Science ®Google Scholar
• Ulfarsson, G. F., & Mannering, F. L. (2004). Differences in male and female injury severities in sport-utility vehicle, minivan, pickup and passenger car accidents. Accident Analysis & Prevention, 36, 135–147. doi:10.1016/S0001-4575(02)00135-5
   PubMed Web of Science ®Google Scholar
• Wang, X., & Abdel-Aty, M. (2008). Analysis of left-turn crash injury severity by conflicting pattern using partial proportional odds models. Accident Analysis & Prevention, 40, 1674–1682. doi:10.1016/j.aap.2008.06.001
   PubMed Web of Science ®Google Scholar
• Wang, Z., Chen, H., & Lu, J. (2009). Exploring impacts of factors contributing to injury severity at freeway diverge areas. Transportation Research Record: Journal of the Transportation Research Board, 2102, 43–52. doi:10.3141/2102-06
   Web of Science ®Google Scholar
• Weiss, H. B., Kaplan, S., & Prato, C. G. (2014). Analysis of factors associated with injury severity in crashes involving young New Zealand drivers. Accident Analysis & Prevention, 65, 142–155. doi:10.1016/j.aap.2013.12.020
   PubMed Web of Science ®Google Scholar
• Wu, Q., Chen, F., Zhang, G., Liu, X. C., Wang, H., & Bogus, S. M. (2014). Mixed logit model-based driver injury severity investigations in single- and multi-vehicle crashes on rural two-lane highways. Accident Analysis & Prevention, 72, 105–115. doi:10.1016/j.aap.2014.06.014
   PubMed Web of Science ®Google Scholar
• Wu, Q., Zhang, G., Zhu, X., 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
• Xie, Y., Zhang, Y., & Liang, F. (2009). Crash injury severity analysis using Bayesian ordered probit models. Journal of Transportation Engineering, 135, 18–25. doi:10.1061/(ASCE)0733-947X(2009)135:1(18)
   Web of Science ®Google Scholar
• Xie, Y., Zhao, K., & Huynh, N. (2012). Analysis of driver injury severity in rural single-vehicle crashes. Accident Analysis & Prevention, 47, 36–44. doi:10.1016/j.aap.2011.12.012
   PubMed Web of Science ®Google Scholar
• Xu, C., Tarko, A. P., Wang, W., & Liu, P. (2013). Predicting crash likelihood and severity on freeways with real-time loop detector data. Accident Analysis & Prevention, 57, 30–39. doi:10.1016/j.aap.2013.03.035
   PubMed Web of Science ®Google Scholar
• Yamamoto, T., Hashiji, J., & Shankar, V. N. (2008). Underreporting in traffic accident data, bias in parameters and the structure of injury severity models, Accid. Accident Analysis & Prevention, 40, 1320–1329. doi:10.1016/j.aap.2007.10.016
   PubMed Web of Science ®Google Scholar
• Yan, X., Harb, R., & Radwan, E. (2008). Analyses of factors of crash avoidance maneuvers using the general estimates system. Traffic Injury Prevention, 9, 173–180. doi:10.1080/15389580701869356
   PubMed Web of Science ®Google Scholar
• Yasmin, S., & Eluru, N. (2013). Evaluating alternate discrete outcome frameworks for modeling crash injury severity. Accident Analysis & Prevention, 59, 506–521. doi:10.1016/j.aap.2013.06.040
   PubMed Web of Science ®Google Scholar
• Yasmin, S., Eluru, N., Bhat, C. R., & Tay, R. (2014). A latent segmentation based generalized ordered logit model to examine factors influencing driver injury severity. Analytic Methods in Accident Research, 1, 23–38. doi:10.1016/j.amar.2013.10.002
   Google Scholar
• Yasmin, S., Eluru, N., & Pinjari, A. R. (2015). Analyzing the continuum of fatal crashes: A generalized ordered approach. Analytic Methods in Accident Research, 7, 1–15. doi:10.1016/j.amar.2015.03.001
   Google Scholar
• Yasmin, S., Eluru, N., Pinjari, A. R., & Tay, R. (2014). Examining driver injury severity in two vehicle crashes–A copula based approach. Accident Analysis & Prevention, 66, 120–135. doi:10.1016/j.aap.2014.01.018
   PubMed Web of Science ®Google Scholar
• Ye, F., & Lord, D. (2014). Comparing three commonly used crash severity models on sample size requirements: Multinomial logit, ordered probit and mixed logit models. Analytic Methods in Accident Research, 1, 72–85. doi:10.1016/j.amar.2013.03.001
   Google Scholar
• Yu, R., & Abdel-Aty, M. (2014). Using hierarchical Bayesian binary probit models to analyze crash injury severity on high speed facilities with real-time traffic data. Accident Analysis & Prevention, 62, 161–167. doi:10.1016/j.aap.2013.08.009
   PubMed Web of Science ®Google Scholar
• Zhang, G., Yau, K. K. W., & Chen, G. (2013). Risk factors associated with traffic violations and accident severity in China. Accident Analysis & Prevention, 59, 18–25. doi:10.1016/j.aap.2013.05.004
   PubMed Web of Science ®Google Scholar
• Zhang, J., Lindsay, J., Clarke, K., Robbins, G., & Mao, Y. (2000). Factors affecting the severity of motor vehicle traffic crashes involving elderly drivers in Ontario. Accident Analysis & Prevention, 32, 117–125. doi:10.1016/S0001-4575(99)00039-1
   PubMed Web of Science ®Google Scholar
• Zhu, X., & Srinivasan, S. (2011a). Modeling occupant-level injury severity: An application to large-truck crashes. Accident Analysis & Prevention, 43, 1427–1437. doi:10.1016/j.aap.2011.02.021
   PubMed Web of Science ®Google Scholar
• Zhu, X., & Srinivasan, S. (2011b). A comprehensive analysis of factors influencing the injury severity of large-truck crashes. Accident Analysis & Prevention, 43, 49–57. doi:10.1016/j.aap.2010.07.007
   PubMed Web of Science ®Google Scholar