| [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. |
| [3] |
Azimi G, Rahimi A, Asgari H, Jin X. 2020. Severity analysis for large truck rollover crashes using a random parameter ordered logit model. |
| [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. |
| [5] |
Wang C, Chen F, Zhang Y, Cheng J. 2022. Spatiotemporal instability analysis of injury severities in truck-involved and non-truck-involved crashes. |
| [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. |
| [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. |
| [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. |
| [9] |
Li D, Al-Mahamda MFM, Song Y, Feng S, Sze NN. 2022. An alternate crash severity multicategory modeling approach with asymmetric property. |
| [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. |
| [11] |
Munira S, Sener IN, Dai B. 2020. A Bayesian spatial Poisson-lognormal model to examine pedestrian crash severity at signalized intersections. |
| [12] |
Fountas G, Anastasopoulos PC. 2018. Analysis of accident injury-severity outcomes: The zero-inflated hierarchical ordered probit model with correlated disturbances. |
| [13] |
Iranitalab A, Khattak A. 2017. Comparison of four statistical and machine learning methods for crash severity prediction. |
| [14] |
Arteaga C, Paz A, Park J. 2020. Injury severity on traffic crashes: a text mining with an interpretable machine-learning approach. |
| [15] |
Yu R, Abdel-Aty M. 2014. Analyzing crash injury severity for a mountainous freeway incorporating real-time traffic and weather data. |
| [16] |
Alkheder S, Taamneh M, Taamneh S. 2017. Severity prediction of traffic accident using an artificial neural network. |
| [17] |
Kang Y, Khattak AJ. 2022. Deep learning model for crash injury severity analysis using Shapley additive explanation values. |
| [18] |
Cai Q. 2020. Cause analysis of traffic accidents on urban roads based on an improved association rule mining algorithm. |
| [19] |
Li J, Liu J, Liu P, Qi Y. 2020. Analysis of factors contributing to the severity of large truck crashes. |
| [20] |
Tang J, Liang J, Han C, Li Z, Huang H. 2019. Crash injury severity analysis using a two-layer stacking framework. |
| [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. |
| [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. |
| [24] |
Toran Pour A, Moridpour S, Tay R, Rajabifard A. 2017. Neighborhood influences on vehicle-pedestrian crash severity. |
| [25] |
Yan M, Shen Y. 2022. Traffic accident severity prediction based on random forest. |
| [26] |
Das S, Dutta A, Dey K, Jalayer M, Mudgal A. 2020. Vehicle involvements in hydroplaning crashes: Applying interpretable machine learning. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [33] |
Abegaz T, Gebremedhin S. 2019. Magnitude of road traffic accident related injuries and fatalities in Ethiopia. |
| [34] |
Folkard S. 1997. Black times: Temporal determinants of transport safety. |
| [35] |
Zhai X, Huang H, Sze NN, Song Z, Hon KK. 2019. Diagnostic analysis of the effects of weather condition on pedestrian crash severity. |
| [36] |
Chandrakumar D, Dorrian J, Banks S, Keage HAD, Coussens S, et al. 2020. The relationship between alertness and spatial attention under simulated shiftwork. |
| [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. |
| [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. |
| [39] |
Edwards JB. 1999. Speed adjustment of motorway commuter traffic to inclement weather. |
| [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. |
| [41] |
Males MA. 2009. Poverty as a determinant of young drivers' fatal crash risks. |
| [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. |
| [44] |
Zhang G, Yau KKW, Zhang X. 2014. Analyzing fault and severity in pedestrian-motor vehicle accidents in China. |
| [45] |
Lemp JD, Kockelman KM, Unnikrishnan A. 2011. Analysis of large truck crash severity using heteroskedastic ordered probit models. |
| [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. |
| [47] |
Theofilatos A, Graham D, Yannis G. 2012. Factors affecting accident severity inside and outside urban areas in Greece. |