[1]

World Health Organization (WHO). 2021. WHO kicks off a decade of action for road safety. www.who.int/news/item/28-10-2021-who-kicks-off-a-decade-of-action-for-road-safety (Accessed on January 7, 2022)

[2]

Wang W, Yang Y, Yang X, Gayah VV, Wang Y, et al. 2024. A negative binomial Lindley approach considering spatiotemporal effects for modeling traffic crash frequency with excess zeros. Accident Analysis & Prevention 207:107741

doi: 10.1016/j.aap.2024.107741
[3]

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
[4]

Meng F, Sze NN, 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

doi: 10.1016/j.amar.2021.100168
[5]

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

doi: 10.1016/j.amar.2022.100214
[6]

Wang C, Chen F, Zhang Y, Wang S, Yu B, et al. 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

doi: 10.1016/j.amar.2022.100219
[7]

Chang F, Haque MM, Yasmin S, Huang H. 2022. Crash injury severity analysis of E-bike riders: a random parameters generalized ordered probit model with heterogeneity in means. Safety Science 146:105545

doi: 10.1016/j.ssci.2021.105545
[8]

Ahmed SS, Alnawmasi N, Anastasopoulos PC, Mannering F. 2022. The effect of higher speed limits on crash-injury severity rates: a correlated random parameters bivariate tobit approach. Analytic Methods in Accident Research 34:100213

doi: 10.1016/j.amar.2022.100213
[9]

Li D, Al-Mahamda MFM, Song Y, Feng S, Sze NN. 2022. An alternate crash severity multicategory modeling approach with asymmetric property. Analytic Methods in Accident Research 35:100218

doi: 10.1016/j.amar.2022.100218
[10]

Yu M, Ma C, Shen J. 2021. Temporal stability of driver injury severity in single-vehicle roadway departure crashes: a random thresholds random parameters hierarchical ordered probit approach. Analytic Methods in Accident Research 29:100144

doi: 10.1016/j.amar.2020.100144
[11]

Munira S, Sener IN, Dai B. 2020. A Bayesian spatial Poisson-lognormal model to examine pedestrian crash severity at signalized intersections. Accident Analysis & Prevention 144:105679

doi: 10.1016/j.aap.2020.105679
[12]

Fountas G, Anastasopoulos PC. 2018. Analysis of accident injury-severity outcomes: The zero-inflated hierarchical ordered probit model with correlated disturbances. Analytic Methods in Accident Research 20:30−45

doi: 10.1016/j.amar.2018.09.002
[13]

Iranitalab A, Khattak A. 2017. Comparison of four statistical and machine learning methods for crash severity prediction. Accident Analysis & Prevention 108:27−36

doi: 10.1016/j.aap.2017.08.008
[14]

Arteaga C, Paz A, Park J. 2020. Injury severity on traffic crashes: a text mining with an interpretable machine-learning approach. Safety Science 132:104988

doi: 10.1016/j.ssci.2020.104988
[15]

Yu R, Abdel-Aty M. 2014. Analyzing crash injury severity for a mountainous freeway incorporating real-time traffic and weather data. Safety Science 63:50−56

doi: 10.1016/j.ssci.2013.10.012
[16]

Alkheder S, Taamneh M, Taamneh S. 2017. Severity prediction of traffic accident using an artificial neural network. Journal of Forecasting 36:100−8

doi: 10.1002/for.2425
[17]

Kang Y, Khattak AJ. 2022. Deep learning model for crash injury severity analysis using Shapley additive explanation values. Transportation Research Record 2676:242−54

doi: 10.1177/03611981221095087
[18]

Cai Q. 2020. Cause analysis of traffic accidents on urban roads based on an improved association rule mining algorithm. IEEE Access 8:75607−15

doi: 10.1109/ACCESS.2020.2988288
[19]

Li J, Liu J, Liu P, Qi Y. 2020. Analysis of factors contributing to the severity of large truck crashes. Entropy 22:1191

doi: 10.3390/e22111191
[20]

Tang J, Liang J, Han C, Li Z, Huang H. 2019. Crash injury severity analysis using a two-layer stacking framework. Accident Analysis & Prevention 122:226−38

doi: 10.1016/j.aap.2018.10.016
[21]

Jamal A, Zahid M, Tauhidur Rahman M, Al-Ahmadi HM, Almoshaogeh M, et al. 2021. Injury severity prediction of traffic crashes with ensemble machine learning techniques: a comparative study. International Journal of Injury Control and Safety Promotion 28:408−27

doi: 10.1080/17457300.2021.1928233
[22]

Ke G, Meng Q, Finley T, Wang T, Chen W, et al. 2017. LightGBM: a highly efficient gradient boosting decision tree. 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, 4−9 December 2017. New York, US: Curran Associates, Inc. www.microsoft.com/en-us/research/wp-content/uploads/2017/11/lightgbm.pdf

[23]

Zhu S, Wang K, Li C. 2021. Crash injury severity prediction using an ordinal classification machine learning approach. International Journal of Environmental Research and Public Health 18:11564

doi: 10.3390/ijerph182111564
[24]

Toran Pour A, Moridpour S, Tay R, Rajabifard A. 2017. Neighborhood influences on vehicle-pedestrian crash severity. Journal of Urban Health 94:855−68

doi: 10.1007/s11524-017-0200-z
[25]

Yan M, Shen Y. 2022. Traffic accident severity prediction based on random forest. Sustainability 14:1729

doi: 10.3390/su14031729
[26]

Das S, Dutta A, Dey K, Jalayer M, Mudgal A. 2020. Vehicle involvements in hydroplaning crashes: Applying interpretable machine learning. Transportation Research Interdisciplinary Perspectives 6:100176

doi: 10.1016/j.trip.2020.100176
[27]

Ma Z, Mei G, Cuomo S. 2021. An analytic framework using deep learning for prediction of traffic accident injury severity based on contributing factors. Accident Analysis & Prevention 160:106322

doi: 10.1016/j.aap.2021.106322
[28]

Zhou B, Wang X, Zhang S, Li Z, Sun S, et al. 2020. Comparing factors affecting injury severity of passenger car and truck drivers. IEEE Access 8:153849−61

doi: 10.1109/ACCESS.2020.3018183
[29]

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
[30]

Guo M, Yuan Z, Janson B, Peng Y, Yang Y, et al. 2021. Older pedestrian traffic crashes severity analysis based on an emerging machine learning XGBoost. Sustainability 13:926

doi: 10.3390/su13020926
[31]

Prokhorenkova L, Gusev G, Vorobev A. 2018. CatBoost: unbiased boosting with categorical features. In 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montréal, Canada, 3−8 December 2018, Montreal, Canada. New York, US: Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2018/file/14491b756b3a51daac41c24863285549-Paper.pdf

[32]

Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, et al. 2020. From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence 2:56−67

doi: 10.1038/s42256-019-0138-9
[33]

Abegaz T, Gebremedhin S. 2019. Magnitude of road traffic accident related injuries and fatalities in Ethiopia. PLOS One 14:e0202240

doi: 10.1371/journal.pone.0202240
[34]

Folkard S. 1997. Black times: Temporal determinants of transport safety. Accident Analysis & Prevention 29:417−30

doi: 10.1016/S0001-4575(97)00021-3
[35]

Zhai X, Huang H, Sze NN, Song Z, Hon KK. 2019. Diagnostic analysis of the effects of weather condition on pedestrian crash severity. Accident Analysis & Prevention 122:318−24

doi: 10.1016/j.aap.2018.10.017
[36]

Chandrakumar D, Dorrian J, Banks S, Keage HAD, Coussens S, et al. 2020. The relationship between alertness and spatial attention under simulated shiftwork. Scientific Reports 10:14946

doi: 10.1038/s41598-020-71800-6
[37]

Lacherez P, Wood JM, Marszalek RP, King MJ. 2013. Visibility-related characteristics of crashes involving bicyclists and motor vehicles – Responses from an online questionnaire study. Transportation Research Part F: Traffic Psychology and Behaviour 20:52−58

doi: 10.1016/j.trf.2013.04.003
[38]

Liang M, Zhao D, Wu Y, Ye P, Wang Y, et al. 2021. Short-term effects of ambient temperature and road traffic accident injuries in Dalian, Northern China: a distributed lag nonlinear analysis. Accident Analysis & Prevention 153:106057

doi: 10.1016/j.aap.2021.106057
[39]

Edwards JB. 1999. Speed adjustment of motorway commuter traffic to inclement weather. Transportation Research Part F: Traffic Psychology and Behaviour 2:1−14

doi: 10.1016/S1369-8478(99)00003-0
[40]

Chang F, Xu P, Zhou H, Lee J, Huang H. 2019. Identifying motorcycle high-risk traffic scenarios through interactive analysis of driver behavior and traffic characteristics. Transportation Research Part F: Traffic Psychology and Behaviour 62:844−54

doi: 10.1016/j.trf.2019.03.010
[41]

Males MA. 2009. Poverty as a determinant of young drivers' fatal crash risks. Journal of Safety Research 40:443−48

doi: 10.1016/j.jsr.2009.10.001
[42]

Van Niekerk A, Du Toit N, Nowell MJ. 2004. Childhood burn injury: epidemiological, management and emerging injury prevention studies. In Crime, Violence and Injury Prevention in South Africa: Developments and Challenges. Tygerberg, South Africa: Violence and Injury Lead Programme, Medical Research Council, University of South Africa Crime. pp. 145−58

[43]

Li H, Wu D, Graham DJ, Sze NN. 2020. Comparison of exposure in pedestrian crash analyses: A study based on zonal origin-destination survey data. Safety Science 131:104926

doi: 10.1016/j.ssci.2020.104926
[44]

Zhang G, Yau KKW, Zhang X. 2014. Analyzing fault and severity in pedestrian-motor vehicle accidents in China. Accident Analysis & Prevention 73:141−50

doi: 10.1016/j.aap.2014.08.018
[45]

Lemp JD, Kockelman KM, Unnikrishnan A. 2011. Analysis of large truck crash severity using heteroskedastic ordered probit models. Accident Analysis & Prevention 43:370−80

doi: 10.1016/j.aap.2010.09.006
[46]

Sun Z, Xing Y, Wang J, Gu X, Lu H, et al. 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
[47]

Theofilatos A, Graham D, Yannis G. 2012. Factors affecting accident severity inside and outside urban areas in Greece. Traffic Injury Prevention 13:458−67

doi: 10.1080/15389588.2012.661110