Contributing factors on the level of delay caused by crashes: a hybrid method of latent class analysis and XGBoost based SHAP algorithm

References

• Adanu, E. K., Brown, D., Jones, S., & Parrish, A. (2021). How did the COVID-19 pandemic affect road crashes and crash outcomes in Alabama? Accident; Analysis and Prevention, 163, 106428. doi:10.1016/j.aap.2021.106428
   PubMed Web of Science ®Google Scholar
• Adeyemia, O. J., Arifa, A., & Paula, R. (2021). Exploring the relationship of rush hour period and fatal and non-fatal crash injuries in the U.S.: A systematic review and meta-analysis. Accident Analysis and Prevention, 163, 106462. doi:10.1016/j.aap.2021.106462
   PubMed Web of Science ®Google Scholar
• Ayoub, J., Yang, X. J., & Zhou, F. (2021). Modeling dispositional and initial learned trust in automated vehicles with predictability and explainability. Transportation Research Part F: Traffic Psychology and Behaviour, 77, 102–116. doi:10.1016/j.trf.2020.12.015
   Web of Science ®Google Scholar
• Badr, W. (2019). Why feature correlation matters. A Lot! Towards Data Science.
   Google Scholar
• Bijmolt, T. H. A., Paas, L. J., & Vermunt, J. K. (2004). Country and consumer segmentation: multi-level latent class analysis of financial product ownership. International Journal of Research in Marketing, 21(4), 323–340. doi:10.1016/j.ijresmar.2004.06.002
   Web of Science ®Google Scholar
• Bil, M., Andrasik, R., & Janoska, Z. (2013). Identification of hazardous road locations of traffic accidents by means of kernel density estimation and cluster significance evaluation. Accident Analysis & Prevention, 55, 265–273. doi:10.1016/j.aap.2013.03.003
   PubMed Web of Science ®Google Scholar
• Black, A. W., Villarini, G., & Mote, T. L. (2017). Effects of rainfall on vehicle crashes in six US states. Weather, Climate, and Society, 9(1), 53–70. doi:10.1175/WCAS-D-16-0035.1
   Web of Science ®Google Scholar
• Boufous, S., Finch, C., Hayen, A., & Williamson, A. (2008). The impact of environmental, vehicle and driver characteristics on injury severity in older drivers hospitalized as a result of a traffic crash. Journal of Safety Research, 39(1), 65–72. doi:10.1016/j.jsr.2007.10.010
   PubMed Web of Science ®Google Scholar
• Cao, Y., Li, S. W., & Fu, C. Y. (2020). An assessment method of urban traffic crash severity considering traveling delay and non-essential fuel consumption of third parties. Sustainability, 12(17), 6806. doi:10.3390/su12176806
   Web of Science ®Google Scholar
• Chand, S., & Dixit, V. V. (2018). Application of Fractal theory for crash rate prediction: Insights from random parameters and latent class tobit models. Accident Analysis & Prevention, 112, 30–38. doi:10.1016/j.aap.2017.12.023
   PubMed Web of Science ®Google Scholar
• Chang, F. R., Xu, P. P., Zhou, H. C., Chan, A. H. S., & Huang, H. L. (2019). Investigating injury severities of motorcycle riders: A two-step method integrating latent class cluster analysis and random parameters logit model. Accident Analysis & Prevention, 131, 316–326. doi:10.1016/j.aap.2019.07.012
   PubMed Web of Science ®Google Scholar
• Chen, T., Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
   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. doi:10.1016/j.aap.2018.10.020
   PubMed Web of Science ®Google Scholar
• Chipman, M., & Jin, Y. L. (2009). Drowsy drivers: The effect of light and circadian rhythm on crash occurrence. Safety Science, 47(10), 1364–1370. doi:10.1016/j.ssci.2009.03.005
   Web of Science ®Google Scholar
• Clarke, D. D., Ward, P., Bartle, C., & Truman, W. (2006). Young driver accidents in the UK: The influence of age, experience, and time of day. Accident Analysis & Prevention, 38(5), 871–878. doi:10.1016/j.aap.2006.02.013
   PubMed Web of Science ®Google Scholar
• Claros, B., Chitturi, M., Vorhes, G., Bill, A., & Noyce, D. (2020). Seasonal crash prediction model for urban signalized intersections: Wisconsin southeast region. Traffic Injury Prevention, 21(7), 447–452. doi:10.1080/15389588.2020.1780429
   PubMed Web of Science ®Google Scholar
• Das, S., Avelar, R., Dixon, K., & Sun, X. D. (2018). Investigation on the wrong way driving crash patterns using multiple correspondence analysis. Accident Analysis & Prevention, 111, 43–55. doi:10.1016/j.aap.2017.11.016
   PubMed Web of Science ®Google Scholar
• de Ona, J., Lopez, G., Mujalli, R., & Calvo, F. J. (2013). Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks. Accident; Analysis and Prevention, 51, 1–10. doi:10.1016/j.aap.2012.10.016
   PubMed Web of Science ®Google Scholar
• De Vos, J. (2020). The effect of COVID-19 and subsequent social distancing on travel behavior. Transportation Research Interdisciplinary Perspectives, 5, 100121. doi:10.1016/j.trip.2020.100121.
   PubMedGoogle 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
• Devlin, A., Beck, B., Simpson, P. M., Ekegren, C. L., Giummarra, M. J., Edwards, E. R., Cameron, P. A., Liew, S., Oppy, A., Richardson, M., Page, R., & Gabbe, B. J. (2019). The road to recovery for vulnerable road users hospitalised for orthopaedic injury following an on-road crash. Accident; Analysis and Prevention, 132, 105279. doi:10.1016/j.aap.2019.105279
   PubMed Web of Science ®Google Scholar
• Federal Highway Administration (FHWA). (2005). Traffic congestion and reliability trends and advanced strategies for congestion mitigation. https://ops.fhwa.dot.gov/congestion_report/congestion_report_05.pdf
   Google Scholar
• Garib, A., Radwan, A. E., & Al-Deek, H. (1997). Estimating magnitude and duration of incident delays. Journal of Transportation Engineering, 123(6), 459–466. doi:10.1061/(ASCE)0733-947X(1997)123:6(459)
   Web of Science ®Google Scholar
• Ghomi, H., & Hussein, M. (2021). An integrated clustering and copula-based model to assess the impact of intersection characteristics on violation-related collisions. Accident; Analysis and Prevention, 159, 106283. doi:10.1016/j.aap.2021.106283
   PubMed Web of Science ®Google Scholar
• Ghosh, I., Savolainen, P. T., & Gates, T. J. (2014). Examination of factors affecting freeway incident clearance times: a comparison of the generalized F model and several alternative nested models. Journal of Advanced Transportation, 48(6), 471–485. doi:10.1002/atr.1189
   Web of Science ®Google Scholar
• Gkritza, K., Kinzenbaw, C. R., Hallmark, S., & Hawkins, N. (2010). An empirical analysis of farm vehicle crash injury severities on Iowa’s public road system. Accident Analysis & Prevention, 42(4), 1392–1397. doi:10.1016/j.aap.2010.03.003
   PubMed Web of Science ®Google Scholar
• Goel, R., Jain, P., & Tiwari, G. (2018). Correlates of fatality risk of vulnerable road users in Delhi. Accident; Analysis and Prevention, 111, 86–93. doi:10.1016/j.aap.2017.11.023
   PubMed Web of Science ®Google Scholar
• Gu, Z. N., & Peng, B. B. (2021). Investigation into the built environment impacts on pedestrian crash frequencies during morning, noon/afternoon, night, and during peak hours: a case study in Miami County. Journal of Transportation Safety & Security, 13(9), 915–935. doi:10.1080/19439962.2019.1701164
   Web of Science ®Google Scholar
• Haule, H. J., Alluri, P., Sando, T., & Raihan, M. A. (2020). Investigating the impact of rain on crash-clearance duration. Journal of Transportation Engineering Part A-Systems, 146(11), 04020130.
   Web of Science ®Google Scholar
• Haule, H. J., Sando, T., Lentz, R., Chuan, C.-H., & Alluri, P. (2019). Evaluating the impact and clearance duration of freeway incidents. International Journal of Transportation Science and Technology, 8(1), 13–24. doi:10.1016/j.ijtst.2018.06.005
   Google Scholar
• Heqimi, G., Gates, T. J., & Kay, J. J. (2018). Using spatial interpolation to determine impacts of annual snowfall on traffic crashes for limited access freeway segments. Accident; Analysis and Prevention, 121, 202–212. doi:10.1016/j.aap.2018.09.014
   PubMed Web of Science ®Google Scholar
• Heydari, S., Fu, L., Miranda-Moreno, L. F., & Jopseph, L. (2017). Using a flexible multivariate latent class approach to model correlated outcomes: A joint analysis of pedestrian and cyclist injuries. Analytic Methods in Accident Research, 13, 16–27. doi:10.1016/j.amar.2016.12.002
   Web of Science ®Google Scholar
• Iranitalab, A., & Khattak, A. (2017). Comparison of four statistical and machine learning methods for crash severity prediction. Accident; Analysis and Prevention, 108, 27–36. doi:10.1016/j.aap.2017.08.008
   PubMed Web of Science ®Google Scholar
• Islam, N., Adanu, E. K., Hainen, A. M., Burdette, S., Smith, R., & Jones, S. (2021). A comparative analysis of freeway crash incident clearance time using random parameter and latent class hazard-based duration model. Accident; Analysis and Prevention, 160, 106303. doi:10.1016/j.aap.2021.106303
   PubMed Web of Science ®Google Scholar
• Jeong, H., 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. doi:10.1016/j.aap.2018.08.025
   PubMed Web of Science ®Google Scholar
• Junhua, W., Haozhe, C., & Shi, Q. (2013). Estimating freeway incident duration using accelerated failure time modeling. Safety Science, 54, 43–50. doi:10.1016/j.ssci.2012.11.009
   Web of Science ®Google Scholar
• Kamel, M. B., & 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
• Kang, Y., & Khattak, A. (2017). Cluster-based approach to analyzing crash injury severity at highway-rail grade crossings. Transportation Research Record: Journal of the Transportation Research Board, 2608(1), 58–69. doi:10.3141/2608-07
   Google Scholar
• Kayes, M. I., Al-Deek, H., Sandt, A., Carrick, G., Staves, C., Gamaleldin, G., Faruk, O., & Al-Sahili, O. (2019). Characteristics of law enforcement response to wrong-way driving events in Florida. Transportation Research Record: Journal of the Transportation Research Board, 2673(9), 514–524. doi:10.1177/0361198119846456
   Web of Science ®Google Scholar
• Keall, M. D., Frith, W. J., & Patterson, T. L. (2005). The contribution of alcohol to night time crash risk and other risks of night driving. Accident; Analysis and Prevention, 37(5), 816–824. doi:10.1016/j.aap.2005.03.021
   PubMed Web of Science ®Google Scholar
• Kitali, A. E., Alluri, P., Sando, T., Haule, H., Kidando, E., & Lentz, R. (2018). Likelihood estimation of secondary crashes using Bayesian complementary log-log model. Accident Analysis & Prevention, 119, 58–67. doi:10.1016/j.aap.2018.07.003
   PubMed Web of Science ®Google Scholar
• Lanza, S. T., & Rhoades, B. L. (2013). Latent class analysis: An alternative perspective on subgroup analysis in prevention and treatment. Prevention Science, 14(2), 157–168. doi:10.1007/s11121-011-0201-1
   PubMed Web of Science ®Google Scholar
• Lee, J., Abdel-Aty, M., Cai, Q., & Wang, L. (2018). Analysis of fatal traffic crash-reporting and reporting-arrival time intervals of emergency medical services. Transportation Research Record: Journal of the Transportation Research Board, 2672(32), 61–71. doi:10.1177/0361198118772724
   Web of Science ®Google Scholar
• Lee, J., Abdel-Aty, M., Cai, Q., & Wang, L. (2018). Effects of emergency medical services times on traffic injury severity: A random-effects ordered probit approach. Traffic Injury Prevention, 19(6), 577–581. doi:10.1080/15389588.2018.1468889
   PubMed Web of Science ®Google Scholar
• Lee, J.-T., & Fazio, J. (2005). Influential factors in freeway crash response and clearance times by emergency management services in peak periods. Traffic Injury Prevention, 6(4), 331–339. doi:10.1080/15389580500255773
   PubMedGoogle Scholar
• Li, Z., Chen, C., Ci, Y., Zhang, G., Wu, Q., Liu, C., & Qian, Z. (2018). Examining driver injury severity in intersection-related crashes using cluster analysis and hierarchical Bayesian models. Accident Analysis & Prevention, 120, 139–151. doi:10.1016/j.aap.2018.08.009
   PubMed Web of Science ®Google Scholar
• Li, Z., Chen, C., Wu, Q., Zhang, G., Liu, C., Prevedouros, P. D., & Ma, D. T. (2018). Exploring driver injury severity patterns and causes in low visibility related single-vehicle crashes using a finite mixture random parameters model. Analytic Methods in Accident Research, 20, 1–14. doi:10.1016/j.amar.2018.08.001
   Web of Science ®Google Scholar
• Linzer, D. A., & Lewis, J. B. (2011). poLCA: An R package for polytomous variable latent class analysis. Journal of Statistical Software, 42(10), 1–29. doi:10.18637/jss.v042.i10
   Web of Science ®Google Scholar
• Liu, P. F., & 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 & Prevention, 135, 105388. doi:10.1016/j.aap.2019.105388
   PubMed Web of Science ®Google Scholar
• Liu, P., & Fan, W. D. (2021). Analysis of head-on crash injury severity using a partial proportional odds model. Journal of Transportation Safety & Security, 13(7), 714–734. doi:10.1080/19439962.2019.1667933
   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 & Prevention, 129, 230–240. doi:10.1016/j.aap.2019.04.001
   PubMed Web of Science ®Google Scholar
• Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S. I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 56–67. doi:10.1038/s42256-019-0138-9
   PubMedGoogle Scholar
• Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in neural information processing systems, 30, 4765–4774.
   Google Scholar
• Ma, X., Ding, C., Luan, S., Wang, Y., & Wang, Y. (2017). Prioritizing influential factors for freeway incident clearance time prediction using the gradient boosting decision trees method. IEEE Transactions on Intelligent Transportation Systems, 18(9), 2303–2310. doi:10.1109/TITS.2016.2635719
   Web of Science ®Google Scholar
• Mannering, F. L., & Bhat, C. R. (2014). Analytic methods in accident research: Methodological frontier and future directions. Analytic Methods in Accident Research, 1, 1–22. doi:10.1016/j.amar.2013.09.001
   Google Scholar
• Mannering, F., Bhat, C. R., Shankar, V., & Abdel-Aty, M. (2020). Big data, traditional data and the tradeoffs between prediction and causality in highway-safety analysis. Analytic Methods in Accident Research, 25, 100113. doi:10.1016/j.amar.2020.100113
   Web of Science ®Google Scholar
• Mannering, F. L., Shankar, V., & Bhat, C. R. (2016). Unobserved heterogeneity and the statistical analysis of highway accident data. Analytic Methods in Accident Research, 11, 1–16. doi:10.1016/j.amar.2016.04.001
   Web of Science ®Google Scholar
• Ma, C., Peng, Y., Wu, L., Guo, X., Wang, X., & Kong, X. (2022). Application of machine learning techniques to predict the occurrence of distraction-affected crashes with phone-use data. Transportation Research Record: Journal of the Transportation Research Board, 2676(2), 692–705. doi:10.1177/03611981211045371
   Web of Science ®Google Scholar
• Meng, Q., & Weng, J. (2013). Uncertainty analysis of accident notification time and emergency medical service response time in work zone traffic accidents. Traffic Injury Prevention, 14(2), 150–158. doi:10.1080/15389588.2012.708886
   PubMed Web of Science ®Google Scholar
• Minge, E. (2013). Emergency medical services response to motor vehicle crashes in rural Areas Publication No. 20-05-Topic, 43–15. doi:10.17226/22503
   Google Scholar
• Møller, M., & Janstrup, K. H. (2021). Crash involvement among unlicensed 17 year old drivers before and after licensing at 17 was allowed. Accident; Analysis and Prevention, 156, 106109. doi:10.1016/j.aap.2021.106109
   PubMed Web of Science ®Google Scholar
• Moosavi, S. (2020). Correlation between severity level and traffic delay. Kaggle. https://www.kaggle.com/sobhanmoosavi/us-accidents/discussion/152370
   Google Scholar
• Moosavi, S., Samavatian, M. H., Parthasarathy, S., Teodorescu, R., Ramnath, R. (2019). Accident risk prediction based on heterogeneous sparse data: New dataset and insights. Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems.
   Google Scholar
• Nafis, S. R., Alluri, P., Wu, W., & Kibria, B. M. G. (2021). Wrong-way driving crash injury analysis on arterial road networks using non-parametric data mining techniques. Journal of Transportation Safety & Security, 14(10), 1–29. doi:10.1080/19439962.2021.1960660
   Web of Science ®Google Scholar
• Nam, D., & Mannering, F. (2000). An exploratory hazard-based analysis of highway incident duration. Transportation Research Part A: Policy and Practice, 34(2), 85–102.
   Web of Science ®Google Scholar
• Omranian, E., & Sharif, H. O. (2018). Evaluation of the global precipitation measurement (GPM) satellite rainfall products over the lower Colorado river basin. JAWRA Journal of the American Water Resources Association, 54(4), 882–898. doi:10.1111/1752-1688.12610
   Web of Science ®Google Scholar
• Omranian, E., Sharif, H., Dessouky, S., & Weissmann, J. (2018). Exploring rainfall impacts on the crash risk on Texas roadways: A crash-based matched-pairs analysis approach. Accident; Analysis and Prevention, 117, 10–20. doi:10.1016/j.aap.2018.03.030
   PubMed Web of Science ®Google Scholar
• Parsa, A. B., Movahedi, A., Taghipour, H., Derrible, S., & Mohammadian, A. (2020). Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis. Accident; Analysis and Prevention, 136, 105405. doi:10.1016/j.aap.2019.105405
   PubMed Web of Science ®Google Scholar
• Penmetsa, P., & Pulugurtha, S. S. (2018). Modeling crash injury severity by road feature to improve safety. Traffic Injury Prevention, 19(1), 102–109. doi:10.1080/15389588.2017.1335396
   PubMed Web of Science ®Google Scholar
• Salum, J. H., Sando, T., Alluri, P., & Kitali, A. (2020). Impact of freeway service patrols on incident clearance duration: Case study of Florida’s road rangers. Journal of Transportation Engineering Part A-Systems, 146(9), 04020094.
   Web of Science ®Google Scholar
• Sasidharan, L., Wu, K.-F., & Menendez, M. (2015). Exploring the application of latent class cluster analysis for investigating pedestrian crash injury severities in Switzerland. Accident Analysis & Prevention, 85, 219–228. doi:10.1016/j.aap.2015.09.020
   PubMed Web of Science ®Google Scholar
• Shapley, L. S. (1953). A value for n-person games, Contributions to Theory Games, 307–317.
   Google Scholar
• Shen, J., Wang, T., Zheng, C., & Yu, H. (2020). Determinants of bicyclist injury severity resulting from crashes at roundabouts, crossroads, and T-junctions. Journal of Advanced Transportation, 2020, 1–12. doi:10.1155/2020/6513128
   Web of Science ®Google Scholar
• Shi, Q., Abdel-Aty, M., & Lee, J. (2016). A Bayesian ridge regression analysis of congestion’s impact on urban expressway safety. Accident; Analysis and Prevention, 88, 124–137. doi:10.1016/j.aap.2015.12.001
   PubMed Web of Science ®Google Scholar
• Shi, X., Wong, Y. D., Li, M. Z., Palanisamy, C., & Chai, C. (2019). A feature learning approach based on XGBoost for driving assessment and risk prediction. Accident; Analysis and Prevention, 129, 170–179. doi:10.1016/j.aap.2019.05.005
   PubMed Web of Science ®Google Scholar
• Sinhaa, A., Chandb, S., Wijayaratnac, K. P., Virdid, N., & Dixite, V. (2020). Comprehensive safety assessment in mixed fleets with connected and automated vehicles: A crash severity and rate evaluation of conventional vehicles. Accident Analysis & Prevention, 142, 105567.
   PubMed Web of Science ®Google Scholar
• Soleimani, S., Mousa, S. R., Codjoe, J., & Leitner, M. (2019). A comprehensive railroad-highway grade crossing consolidation model: A machine learning approach. Accident Analysis & Prevention, 128, 65–77. doi:10.1016/j.aap.2019.04.002
   PubMed Web of Science ®Google Scholar
• Stiles, J., Kar, A., Lee, J., & Miller, H. J. (2021). Lower volumes, higher speeds: Changes to crash type, timing, and severity on urban roads from COVID-19 stay-at-home policies. Transportation Research Record: Journal of the Transportation Research Board. doi:10.1177/03611981211044454
   PubMed Web of Science ®Google Scholar
• Sun, M., Sun, X., & Shan, D. (2019). Pedestrian crash analysis with latent class clustering method. Accident Analysis & Prevention, 124, 50–57. doi:10.1016/j.aap.2018.12.016
   PubMed Web of Science ®Google Scholar
• Sun, Z. Y., Xing, Y. X., Gu, X., & Chen, Y. Y. (2022). Influence factors on injury severity of bicycle-motor vehicle crashes: A two-stage comparative analysis of urban and suburban areas in Beijing. Traffic Injury Prevention, 23(2), 118–124. doi:10.1080/15389588.2021.2024523
   PubMed Web of Science ®Google Scholar
• Sun, Z. Y., Xing, Y. X., Wang, J. Y., Gu, X., Lu, H. P., & Chen, Y. Y. (2021). Exploring injury severity of bicycle-motor vehicle crashes: A two-stage approach integrating latent class analysis and random parameter logit model. Journal of Transportation Safety & Security, 14(11), 1838–1864. doi:10.1080/19439962.2021.1971814
   Web of Science ®Google Scholar
• Sun, Z., Xing, Y., Wang, J., Gu, X., Lu, H., & Chen, Y. (2022). Exploring injury severity of vulnerable road user involved crashes across seasons: A hybrid method integrating random parameter logit model and Bayesian network. Safety Science, 150, 105682. doi:10.1016/j.ssci.2022.105682
   Web of Science ®Google Scholar
• Twaddle, H., & Busch, F. (2019). Binomial and multinomial regression models for predicting the tactical choices of bicyclists at signalised intersections. Transportation Research Part F: Traffic Psychology and Behaviour, 60, 47–57. doi:10.1016/j.trf.2018.10.002
   Web of Science ®Google Scholar
• Vingilis, E., Beirness, D., Boase, P., Byrne, P., Johnson, J., Jonah, B., Mann, R. E., Rapoport, M. J., Seeley, J., Wickens, C. M., & Wiesenthal, D. L. (2020). Coronavirus disease 2019: What could be the effects on Road safety? Accident Analysis & Prevention, 144, 105687. doi:10.1016/j.aap.2020.105687
   PubMed Web of Science ®Google Scholar
• Wali, B., Khattak, A. J., & Liu, J. (2021). Heterogeneity assessment in incident duration modelling: Implications for development of practical strategies for small & large scale incidents. Journal of Intelligent Transportation Systems, 26(5), 586–601. doi:10.1080/15472450.2021.1944135
   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 and Prevention, 40(5), 1674–1682. doi:10.1016/j.aap.2008.06.001
   PubMed Web of Science ®Google Scholar
• Wang, J., Huang, H., Xu, P., Xie, S., & Wong, S. C. (2020). Random parameter probit models to analyze pedestrian red-light violations and injury severity in pedestrian-motor vehicle crashes at signalized crossings. Journal of Transportation Safety & Security, 12(6), 818–837. doi:10.1080/19439962.2018.1551257
   Web of Science ®Google Scholar
• Wang, C., Lu, L., & Lu, J. (2015). Statistical analysis of bicyclists’ injury severity at unsignalized intersections. Traffic Injury Prevention, 16(5), 507–512. doi:10.1080/15389588.2014.969802
   PubMed Web of Science ®Google Scholar
• Wei, Z. H., Das, S., & Zhang, Y. L. (2022). Short duration crash prediction for rural two-lane roadways: Applying explainable artificial intelligence. Transportation Research Record, 2676(12). doi:10.1177/03611981221096113
   PubMed Web of Science ®Google Scholar
• Wen, X., Xie, Y., Jiang, L., Li, Y., & Ge, T. (2022). On the interpretability of machine learning methods in crash frequency modeling and crash modification factor development. Accident; Analysis and Prevention, 168, 106617.
   PubMed Web of Science ®Google Scholar
• Wen, X., Xie, Y., Wu, L., & Jiang, L. (2021). Quantifying and comparing the effects of key risk factors on various types of roadway segment crashes with LightGBM and SHAP. Accident Analysis & Prevention, 159, 106261. doi:10.1016/j.aap.2021.106261
   PubMed Web of Science ®Google Scholar
• Wilkins, D. C., Kockelman, K. M., & Jiang, N. (2019). Animal-vehicle collisions in Texas: How to protect travelers and animals on roadways. Accident; Analysis and Prevention, 131, 157–170. doi:10.1016/j.aap.2019.05.030
   PubMed Web of Science ®Google Scholar
• Wu, Q., Zhang, G., Chen, C., Tarefder, R., Wang, H., & Wei, H. (2016). Heterogeneous impacts of gender-interpreted contributing factors on driver injury severities in single-vehicle rollover crashes. Accident Analysis & Prevention, 94, 28–34. doi:10.1016/j.aap.2016.04.005
   PubMed Web of Science ®Google Scholar
• Yan, X. D., Radwan, E., Birriel, E., & Trb. (2005). Analysis of red light running crashes based on quasi-induced exposure and multiple logistic regression method. Transportation Research Record: Journal of the Transportation Research Board,1908(1), 70–79. doi:10.1177/0361198105190800109.
   Google Scholar
• Yang, C., Chen, M., & Yuan, Q. (2021). The application of XGBoost and SHAP to examining the factors in freight truck-related crashes: An exploratory analysis. Accident Analysis & Prevention, 158, 106153. doi:10.1016/j.aap.2021.106153
   PubMed Web of Science ®Google Scholar
• Yu, R., Wang, X., & Abdel-Aty, M. (2017). A hybrid latent class analysis modeling approach to analyze urban expressway crash risk. Accident; Analysis and Prevention, 101, 37–43. doi:10.1016/j.aap.2017.02.002
   PubMed Web of Science ®Google Scholar
• Zheng, Z., Wang, Z., Zhu, L., & Jiang, H. (2020). Determinants of the congestion caused by a traffic accident in urban road networks. Accident; Analysis and Prevention, 136, 105327. doi:10.1016/j.aap.2019.105327
   PubMed Web of Science ®Google Scholar