References
- Almannaa M, Zawad MN, Moshawah M, et al. Investigating the effect of road condition and vacation on crash severity using machine learning algorithms. Int J Inj Contr Saf Promot. 2023;30(3):392–402. doi: 10.1080/17457300.2023.2202660.
- Ambo TB, Ma J, Fu C, et al. Investigating road conditions of crash blackspots in Addis Ababa, Ethiopia: a random parameter negative binomial model. Int J Crashworth. 2023;29(3):521–532. doi: 10.1080/13588265.2023.2258648.
- Islam M. An empirical analysis on the effects of alcohol-impairment in injury severities of motorcyclists. Int J Crashworth. 2023;29(3):570–582. doi: 10.1080/13588265.2023.2259705.
- Obeidat MS, Khrais SK, Bataineh and BS, et al. Impacts of roadway lighting on traffic crashes and safety in Jordan. Int J Crashworth. 2022;27(2):533–542. doi: 10.1080/13588265.2020.1826788.
- World Health Organization. (2019). Global status report on road safety 2018. World Health Organization.
- Yan X, He J, Zhang C, et al. Temporal analysis of crash-injury severities involving male and female drivers: a random parameter approach with heterogeneity in means and variances. Anal Meth Accid Res. 2021;30:100161. doi: 10.1016/j.amar.2021.100161.
- Behnood A, Mannering F. Determinants of bicyclist injury severities in bicycle-vehicle crashes: a random parameter approach with heterogeneity in means and variances. Anal Meth Accid Res. 2017;16:35–47. doi: 10.1016/j.amar.2017.08.001.
- Behnood A, Al-Bdairi NSS. Determinant of injury severities in large truck crashes: a weekly instability analysis. Saf Sci. 2020;131:104911. doi: 10.1016/j.ssci.2020.104911.
- Mannering F. Temporal instability and the analysis of highway accident data. Anal Meth Accid Res. 2018;17:1–13. doi: 10.1016/j.amar.2017.10.002.
- Yan X, He J, Wu G, et al. Weekly variations and temporal instability of determinants influencing alcohol-impaired driving crashes: a random thresholds random parameters hierarchical ordered probit model. Anal Meth Accid Res. 2021;32:100189. doi: 10.1016/j.amar.2021.100189.
- Yan X, He J, Zhang C, et al. Spatiotemporal instability analysis considering unobserved heterogeneity of crash-injury severities in adverse weather. Anal Meth Accid Res. 2021;32:100182. doi: 10.1016/j.amar.2021.100182.
- Yan X, He J, Wu G, et al. Differences of overturned and hit-fixed-object crashes on rural roads accompanied by speeding driving: accommodating potential temporal shifts. Anal Meth Accid Res. 2022;35:100220. doi: 10.1016/j.amar.2022.100220.
- Nasri M, Aghabayk K. Assessing risk factors associated with urban transit bus involved accident severity: a case study of a Middle East country. Int J Crashworth. 2021;26(4):413–423. doi: 10.1080/13588265.2020.1718465.
- Behnood A, Roshandeh AM, Mannering FL. Latent class analysis of the effects of age, gender, and alcohol consumption on driver-injury severities. Anal Meth Accid Res. 2014;3-4:56–91. doi: 10.1016/j.amar.2014.10.001.
- Hagge RA, Romanowicz PA. Evaluation of California’s commercial driver license program. Accid Anal Prev. 1996;28(5):547–559. doi: 10.1016/0001-4575(96)00013-9.
- Terry L, Madison L, Michael T, et al. Driver injuries in heavy vs. light and medium truck local crashes, 2010–2019. J Saf Res. 2022;83:26–34. doi: 10.1016/j.jsr.2022.08.001.
- Islam M, Mannering F. The role of gender and temporal instability in driver-injury severities in crashes caused by speeds too fast for conditions. Accid Anal Prev. 2021;153:106039. doi: 10.1016/j.aap.2021.106039.
- Li Z, Wu Q, Ci Y, et al. Using latent class analysis and mixed logit model to explore risk factors on driver injury severity in single-vehicle crashes. Accid Anal Prev. 2019;129:230–240. doi: 10.1016/j.aap.2019.04.001.
- Chen Q, Huang H, Li Y, et al. Modeling accident risks in different lane-changing behavioral patterns. Anal Meth Accid Res. 2021;30:100159. doi: 10.1016/j.amar.2021.100159.
- Intini P, Berloco N, Fonzone A, et al. The influence of traffic, geometric and context variables on urban crash types: a grouped random parameter multinomial logit approach. Anal Meth Accid Res. 2020;28:100141. doi: 10.1016/j.amar.2020.100141.
- Kutela B, Kidando E, Kitali AE, et al. Exploring pre-crash gate violations behaviors of drivers at highway-rail grade crossings using a mixed multinomial logit model. Int J Inj Contr Saf Promot. 2022;29(2):226–238. doi: 10.1080/17457300.2021.1990348.
- Pantangi S, Ahmed S, Fountas G, et al. Do high visibility crosswalks improve pedestrian safety? A correlated grouped random parameters approach using naturalistic driving study data. Anal Meth Accid Res. 2021;30:100155. doi: 10.1016/j.amar.2020.100155.
- Greene WH, Hensher DA. Heteroscedastic control for random coefficients and error components in mixed logit. Transp Res Part E: Log Transp Rev. 2007;43(5):610–623. doi: 10.1016/j.tre.2006.02.001.
- Hess S, Train K. Correlation and scale in mixed logit models. J Choice Model. 2017;23:1–8. doi: 10.1016/j.jocm.2017.03.001.
- McFadden D. Econometric models of probabilistic choice. Struct Anal Discrete Data Using Eco Appl. MIT Press, Cambridge, 1981;198–27.
- McFadden D, Train K. Mixed MNL models for discrete response. J Appl Econ. 2000;15(5):447–470. doi: 10.1002/1099-1255(200009/10)15:5<447::AID-JAE570>3.0.CO;2-1.
- Seraneeprakarn P, Huang S, Shankar V, et al. Occupant injury severities in hybrid-vehicle involved crashes: a random parameter approach with heterogeneity in means and variances. Anal Meth Accid Res. 2017;15:41–55. doi: 10.1016/j.amar.2017.05.003.
- Rahimi E, Shamshiripour A, Samimi A, et al. Investigating the injury severity of single-vehicle truck crashes in a developing country. Accid Anal Prev. 2020;137:105444. doi: 10.1016/j.aap.2020.105444.
- McFadden D. Conditional Logit analysis of qualitative choice behavior. In: Zarembka, P., Ed., Frontiers in Econometrics, Academic Press, 1973;105–142.
- Alnawmasi N, Mannering F. A temporal assessment of distracted driving injury severities using alternate unobserved-heterogeneity modeling approaches. Anal Meth Accid Res. 2022;34:100216. doi: 10.1016/j.amar.2022.100216.
- Yu H, Li Z, Zhang G, et al. Fusion convolutional neural network-based interpretation of unobserved heterogeneous factors in driver injury severity outcomes in single-vehicle crashes. Anal Meth Accid Res. 2021;30:100157. doi: 10.1016/j.amar.2021.100157.
- Javid MA, Ali N, Abdullah M, et al. Analysis of driver’s socioeconomic characteristics relating to speeding behavior and crash involvement: a case study in Lahore. Infrastructures. 2022;7(2):18. doi: 10.3390/infrastructures7020018.
- Lajunen T, Karola J, Summala H. Speed and acceleration as measures of driving style in young male drivers. Percept Mot Skills. 1997;85(1):3–16. doi: 10.2466/pms.1997.85.1.3.
- Dutta B, Chakroborty P, Vasudevan V. Study of overtaking maneuvers on wide one-way roads in weak lane-disciplined traffic using naturalistic driving data. Transp Res Rec. 2023;2677(3):565–582. doi: 10.1177/03611981221116365.
- Tait C, Beecham R, Lovelace R, et al. Contraflows and cycling safety: evidence from 22 years of data involving 508 one-way streets. Accid Anal Prev. 2023;179:106895. doi: 10.1016/j.aap.2022.106895.
- Zamani A, Behnood A, Davoodi S. Temporal stability of pedestrian injury severity in pedestrian-vehicle crashes: new insights from random parameter logit model with heterogeneity in means and variances. Anal Meth Accid Res. 2021;32:100184. doi: 10.1016/j.amar.2021.100184.
- Li Z, Chen C, Wu Q, et al. Exploring driver injury severity patterns and causes in low visibility related single-vehicle crashes using a finite mixture random parameters model. Anal Meth Accid Res. 2018;20:1–14. doi: 10.1016/j.amar.2018.08.001.
- Mannering F, Bhat CR, Shankar V, et al. Big data, traditional data and the tradeoffs between prediction and causality in highway-safety analysis. Anal Meth Accid Res. 2020;25:100113. doi: 10.1016/j.amar.2020.100113.
- Mothafer GI, Yamamoto T, Shankar VN. Evaluating crash type covariances and roadway geometric marginal effects using the multivariate Poisson gamma mixture model. Anal Meth Accid Res. 2016;9:16–26. doi: 10.1016/j.amar.2015.11.001.
- Dabbour E, Dabbour O, Martinez AA. Temporal stability of the factors related to the severity of drivers’ injuries in rear-end collisions. Accid Anal Prev. 2020;142:105562. doi: 10.1016/j.aap.2020.105562.
- Adanu EK, Hainen A, Jones S. Latent class analysis of factors that influence weekday and weekend single-vehicle crash severities. Accid Anal Prev. 2018;113:187–192. doi: 10.1016/j.aap.2018.01.035.
- Vankov D, Schroeter R. Driving under the influence of drugs or alcohol: predicting the intentions of young drivers. Traffic Inj Prev. 2021;22(2):97–101. doi: 10.1080/15389588.2020.1869953.
- Marcoux R, Yasmin S, Eluru N, et al. Evaluating temporal variability of exogenous variable impacts over 25 years: an application of scaled generalized ordered logit model for driver injury severity. Anal Meth Accid Res. 2018;20:15–29. doi: 10.1016/j.amar.2018.09.001.
- Yu H, Li Z, Zhang G, et al. A latent class approach for driver injury severity analysis in highway single vehicle crash considering unobserved heterogeneity and temporal influence. Anal Meth Accid Res. 2019;24:100110. doi: 10.1016/j.amar.2019.100110.
- Cerwick DM, Gkritza K, Shaheed MS, et al. A comparison of the mixed logit and latent class methods for crash severity analysis. Anal Meth Accid Res. 2014;3-4:11–27. doi: 10.1016/j.amar.2014.09.002.
- Yasmin S, Eluru N, Bhat C, et al. A latent segmentation based generalized ordered logit model to examine factors influencing driver injury severity. Anal Meth Accid Res. 2014;1:23–38. doi: 10.1016/j.amar.2013.10.002.