[1]

Roshandel S, Zheng Z, Washington S. 2015. Impact of real-time traffic characteristics on freeway crash occurrence: systematic review and meta-analysis. Accident Analysis & Prevention 79:198−211

doi: 10.1016/j.aap.2015.03.013
[2]

Savolainen PT, Mannering FL, Lord D, Quddus MA. 2011. The statistical analysis of highway crash-injury severities: A review and assessment of methodological alternatives. Accident Analysis & Prevention 43(5):1666−76

doi: 10.1016/j.aap.2011.03.025
[3]

Mannering FL, Bhat CR. 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
[4]

Mannering FL, Shankar V, Bhat CR. 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
[5]

Mannering FL. 2018. Temporal instability and the analysis of highway accident data. Analytic Methods in Accident Research 17:1−13

doi: 10.1016/j.amar.2017.10.002
[6]

Chen H, Cao L, Logan DB. 2012. Analysis of risk factors affecting the severity of intersection crashes by logistic regression. Traffic Injury Prevention 13(3):300−7

doi: 10.1080/15389588.2011.653841
[7]

Lao Y, Zhang, G, Wang Y, Milton J. 2014. Generalized nonlinear models for rear-end crash risk analysis. Accident Analysis & Prevention 62:9−16

doi: 10.1016/j.aap.2013.09.004
[8]

Yu R, Wang X, Yang K, Abdel-Aty, M. 2016. Crash risk analysis for Shanghai urban expressways: a Bayesian semi-parametric modeling approach. Accident Analysis & Prevention 95:495−502

doi: 10.1016/j.aap.2015.11.029
[9]

Cunto FJC, Ferreira S. 2017. An analysis of the injury severity of motorcycle crashes in Brazil using mixed ordered response models. Journal of Transportation Safety & Security 9:33−46

doi: 10.1080/19439962.2016.1162891
[10]

Wu Y, Abdel-Aty M, Lee J. 2018. Crash risk analysis during fog conditions using real-time traffic data. Accident Analysis & Prevention 114:4−11

doi: 10.1016/j.aap.2017.05.004
[11]

Gu X, Abdel-Aty M, Xiang Q, Cai Q, Yuan J. 2019. Utilizing UAV video data for in-depth analysis of drivers’ crash risk at interchange merging areas. Accident Analysis & Prevention 123:159−69

doi: 10.1016/j.aap.2018.11.010
[12]

Theofilatos A, Yannis G. 2014. A review of the effect of traffic and weather characteristics on road safety. Accident Analysis & Prevention 72:244−56

doi: 10.1016/j.aap.2014.06.017
[13]

Weng J, Meng Q, Yan X. 2014. Analysis of work zone rear-end crash risk for different vehicle-following patterns. Accident Analysis & Prevention 72:449−57

doi: 10.1016/j.aap.2014.08.003
[14]

Weng J, Xue S, Yang Y, Yan X, Qu X. 2015. In-depth analysis of drivers’ merging behavior and rear-end crash risks in work zone merging areas. Accident Analysis & Prevention 77:51−61

doi: 10.1016/j.aap.2015.02.002
[15]

Dingus TA, Guo F, Lee S, Antin JF, Perez M, et al. 2016. Driver crash risk factors and prevalence evaluation using naturalistic driving data. PNAS, 113(10):2636−41

doi: 10.1073/pnas.1513271113
[16]

Papadimitriou E, Filtness A, Theofilatos A, Ziakopoulos A, Quigley C, et al. 2019. Review and ranking of crash risk factors related to the road infrastructure. Accident Analysis & Prevention 125:85−97

doi: 10.1016/j.aap.2019.01.002
[17]

Wang X, Qu Z, Song X, Bai Q, Pan Z, et al. 2021. Incorporating accident liability into crash risk analysis: A multidimensional risk source approach. Accident Analysis & Prevention 153:106035

doi: 10.1016/j.aap.2021.106035
[18]

Adeyemi OJ, Arif AA, Paul R. 2021. Exploring the relationship of rush hour period and fatal and non-fatal crash injuries in the US: a systematic review and meta-analysis. Accident Analysis & Prevention 163:106462

doi: 10.1016/j.aap.2021.106462
[19]

Mahajan V, Katrakazas C, Antoniou C. 2022. Crash risk estimation due to lane changing: A data-driven approach using naturalistic data. IEEE Transactions on Intelligent Transportation Systems 23(4):3756−65

doi: 10.1109/TITS.2020.3042097
[20]

Papadimitriou E, Theofilatos A. 2017. Meta-analysis of crash-risk factors in freeway entrance and exit areas. Journal of Transportation Engineering, Part A: Systems 143(10):04017050

doi: 10.1061/JTEPBS.0000082
[21]

Asbridge M, Desapriya E, Ogilvie R, Cartwright J, Mehrnoush V, et al. 2017. The impact of restricted driver’s licenses on crash risk for older drivers: a systematic review. Transportation Research Part A: Policy and Practice 97:137−45

doi: 10.1016/j.tra.2017.01.006
[22]

Banz BC, Hersey D, Vaca FE. 2021. Coupling neuroscience and driving simulation: A systematic review of studies on crash-risk behaviors in young drivers. Traffic Injury Prevention 22(1):90−95

doi: 10.1080/15389588.2020.1847283
[23]

Yu R, Abdel-Aty M. 2013. Utilizing support vector machine in real-time crash risk evaluation. Accident Analysis & Prevention 51:252−59

doi: 10.1016/j.aap.2012.11.027
[24]

Yuan J, Abdel-Aty M. 2018. Approach-level real-time crash risk analysis for signalized intersections. Accident Analysis & Prevention 119:274−89

doi: 10.1016/j.aap.2018.07.031
[25]

Yasmin S, Eluru N, Wang L, Abdel-Aty MA. 2018. A joint framework for static and real-time crash risk analysis. Analytic Methods in Accident Research 18:45−66

doi: 10.1016/j.amar.2018.04.001
[26]

Wang L, Abdel-Aty M, Lee J, Shi Q. 2019. Analysis of real-time crash risk for expressway ramps using traffic, geometric, trip generation, and socio-demographic predictors. Accident Analysis & Prevention 122:378−84

doi: 10.1016/j.aap.2017.06.003
[27]

Guo M, Zhao X, Yao Y, Yan P, Su Y, et al. 2021. A study of freeway crash risk prediction and interpretation based on risky driving behavior and traffic flow data. Accident Analysis & Prevention 160:106328

doi: 10.1016/j.aap.2021.106328
[28]

Bao J, Liu P, Ukkusuri SV. 2019. A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data. Accident Analysis & Prevention 122:239−54

doi: 10.1016/j.aap.2018.10.015
[29]

Li P, Abdel-Aty M, Yuan J. 2020. Real-time crash risk prediction on arterials based on LSTM-CNN. Accident Analysis & Prevention 135:105371

doi: 10.1016/j.aap.2019.105371
[30]

Wang C, Xie Y, Huang H, Liu P. 2021. A review of surrogate safety measures and their applications in connected and automated vehicles safety modeling. Accident Analysis & Prevention 157:106157

doi: 10.1016/j.aap.2021.106157
[31]

Qin X, Ivan JN, Ravishanker N. 2004. Selecting exposure measures in crash rate prediction for two-lane highway segments. Accident Analysis & Prevention 36(2):183−91

doi: 10.1016/S0001-4575(02)00148-3
[32]

Caliendo C, Guida M, Parisi A. 2007. A crash-prediction model for multilane roads. Accident Analysis & Prevention 39(4):657−70

doi: 10.1016/j.aap.2006.10.012
[33]

Ma J, Kockelman KM, Damien P. 2008. A multivariate Poisson-lognormal regression model for prediction of crash counts by severity using Bayesian methods. Accident Analysis & Prevention 40(3):964−75

doi: 10.1016/j.aap.2007.11.002
[34]

Hou Q, Huo X, Leng J, Mannering F. 2022. A note on out-of-sample prediction, marginal effects computations, and temporal testing with random parameters crash-injury severity models. Analytic Methods in Accident Research 33:100191

doi: 10.1016/j.amar.2021.100191
[35]

Hossain M, Muromachi Y. 2012. A Bayesian network based framework for real-time crash prediction on the basic freeway segments of urban expressways. Accident Analysis & Prevention 45:373−81

doi: 10.1016/j.aap.2011.08.004
[36]

Sun J, Sun J. 2015. A dynamic Bayesian network model for real-time crash prediction using traffic speed conditions data. Transportation Research Part C: Emerging Technologies 54:176−86

doi: 10.1016/j.trc.2015.03.006
[37]

Dong N, Huang H, Zheng L. 2015. Support vector machine in crash prediction at the level of traffic analysis zones: assessing the spatial proximity effects. Accident Analysis & Prevention 82:192−98

doi: 10.1016/j.aap.2015.05.018
[38]

Huang H, Song B, Xu P, Zeng Q, Lee J, et al. 2016. Macro and micro models for zonal crash prediction with application in hot zones identification. Journal of Transport Geography 54:248−56

doi: 10.1016/j.jtrangeo.2016.06.012
[39]

Tang J, Yin W, Han C, Liu X, Huang H. 2021. A random parameters regional quantile analysis for the varying effect of road-level risk factors on crash rates. Analytic Methods in Accident Research 29:100153

doi: 10.1016/j.amar.2020.100153
[40]

Ambros J, Jurewicz C, Turner S, Kieć M. 2018. An international review of challenges and opportunities in development and use of crash prediction models. European Transport Research Review 10:35

doi: 10.1186/s12544-018-0307-7
[41]

Wu Y, Hsu TP. 2021. Mid-term prediction of at-fault crash driver frequency using fusion deep learning with city-level traffic violation data. Accident Analysis & Prevention 150:105910

doi: 10.1016/j.aap.2020.105910
[42]

Delen D, Tomak L, Topuz K, Eryarsoy E. 2017. Investigating injury severity risk factors in automobile crashes with predictive analytics and sensitivity analysis methods. Journal of Transport & Health 4:118−31

doi: 10.1016/j.jth.2017.01.009
[43]

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
[44]

Huang H, Peng Y, Wang J, Luo Q, Li X. 2018. Interactive risk analysis on crash injury severity at a mountainous freeway with tunnel groups in China. Accident Analysis and Prevention 111:56−62

doi: 10.1016/j.aap.2017.11.024
[45]

Santos K, Dias JP, Amado C. 2022. A literature review of machine learning algorithms for crash injury severity prediction. Journal of Safety Research 80:254−69

doi: 10.1016/j.jsr.2021.12.007
[46]

Li Z, Wu Q, Ci Y, Chen C, Chen X, et al. 2019. Using latent class analysis and mixed logit model to explore risk factors on driver injury severity in single-vehicle crashes. Accident Analysis and Prevention 129:230−40

doi: 10.1016/j.aap.2019.04.001
[47]

Basso F, Pezoa R, Varas M, Villalobos M. 2021. A deep learning approach for real-time crash prediction using vehicle-by-vehicle data. Accident Analysis and Prevention 162:106409

doi: 10.1016/j.aap.2021.106409
[48]

Thapa D, Paleti R, Mishra S. 2022. Overcoming challenges in crash prediction modeling using discretized duration approach: An investigation of sampling approaches. Accident Analysis and Prevention 169:106639

doi: 10.1016/j.aap.2022.106639
[49]

Man CK, Quddus M, Theofilatos A. 2022. Transfer learning for spatio-temporal transferability of real-time crash prediction models. Accident Analysis and Prevention 165:106511

doi: 10.1016/j.aap.2021.106511
[50]

Ma X, Lu J, Liu X, Qu W. 2022. A genetic programming approach for real-time crash prediction to solve trade-off between interpretability and accuracy. Journal of Transportation Safety & Security

doi: 10.1080/19439962.2022.2076756
[51]

Li P, Abdel-Aty M. 2022. Real-time crash likelihood prediction using temporal attention–based deep learning and trajectory fusion. Journal of Transportation Engineering, Part A: Systems 148(7):04022043

doi: 10.1061/JTEPBS.0000697
[52]

Hu Z, Zhou J, Huang K, Zhang E. 2022. A data-driven approach for traffic crash prediction: A case study in Ningbo, China. International Journal of Intelligent Transportation Systems Research 20(2):508−18

doi: 10.1007/s13177-022-00307-3
[53]

Ahmed MM, Abdel-Aty MA. 2011. The viability of using automatic vehicle identification data for real-time crash prediction. IEEE Transactions on Intelligent Transportation Systems 13(2):459−68

doi: 10.1109/tits.2011.2171052
[54]

Lee C, Hellinga B, Saccomanno F. 2003. Proactive freeway crash prevention using real-time traffic control. Canadian Journal of Civil Engineering 30(6):1034−41

doi: 10.1139/l03-040
[55]

Mirzaei R, Hafezi-Nejad N, Sadegh Sabagh M, Ansari Moghaddam A, Eslami V, et al. 2014. Dominant role of drivers’ attitude in prevention of road traffic crashes: A study on knowledge, attitude, and practice of drivers in Iran. Accident Analysis and Prevention 66:36−42

doi: 10.1016/j.aap.2014.01.013
[56]

Ker K, Roberts I, Collier T, Beyer F, Bunn F, et al. 2005. Post-licence driver education for the prevention of road traffic crashes: a systematic review of randomised controlled trials. Accident Analysis and Prevention 37(2):305−13

doi: 10.1016/j.aap.2004.09.004
[57]

El Khoury J, Hobeika A. 2006. Simulation of an ITS crash prevention technology at a no-passing zone site. Journal of Intelligent Transportation Systems 10(2):75−87

doi: 10.1080/15472450600626265
[58]

Chen Z, Qin X. 2019. A novel method for imminent crash prediction and prevention. Accident Analysis and Prevention 125:320−29

doi: 10.1016/j.aap.2018.07.011
[59]

Yue L, Abdel-Aty M, Wu Y, Zheng O, Yuan J. 2020. In-depth approach for identifying crash causation patterns and its implications for pedestrian crash prevention. Journal of Safety Research 73:119−32

doi: 10.1016/j.jsr.2020.02.020
[60]

Hinnant JB, Stavrinos D. 2020. Rewards decrease risky decisions for adolescent drivers: Implications for crash prevention. Transportation Research Part F: Traffic Psychology and Behaviour 74:272−79

doi: 10.1016/j.trf.2020.08.028
[61]

Gidion F, Carroll J, Lubbe N. 2021. Motorcyclist injuries: Analysis of German in-depth crash data to identify priorities for injury assessment and prevention. Accident Analysis and Prevention 163:106463

doi: 10.1016/j.aap.2021.106463
[62]

Peng C, Xu C. 2021. Combined variable speed limit and lane change guidance for secondary crash prevention using distributed deep reinforcement learning. Journal of Transportation Safety & Security 14:2166−91

doi: 10.1080/19439962.2021.2011810
[63]

Jang J, Ko J, Park J, Oh C, Kim S. 2020. Identification of safety benefits by inter-vehicle crash risk analysis using connected vehicle systems data on Korean freeways. Accident Analysis and Prevention 144:105675

doi: 10.1016/j.aap.2020.105675
[64]

Xu C, Ding Z, Wang C, Li Z. 2019. Statistical analysis of the patterns and characteristics of connected and autonomous vehicle involved crashes. Journal of Safety Research 71:41−47

doi: 10.1016/j.jsr.2019.09.001
[65]

Sinha A, Chand S, Wijayaratna KP, Virdi N, Dixit V. 2020. Comprehensive safety assessment in mixed fleets with connected and automated vehicles: A crash severity and rate evaluation of conventional vehicles. Accident Analysis and Prevention 142:105567

doi: 10.1016/j.aap.2020.105567
[66]

Wang L, Zhong H, Ma W, Abdel-Aty M, Park J. 2020. How many crashes can connected vehicle and automated vehicle technologies prevent: a meta-analysis. Accident Analysis and Prevention 136:105299

doi: 10.1016/j.aap.2019.105299
[67]

Xu X, Kwigizile V, Teng H. 2013. Identifying access management factors associated with safety of urban arterials mid-blocks: A panel data simultaneous equation models approach. Traffic Injury Prevention 14(7):734−42

doi: 10.1080/15389588.2012.742515
[68]

Li W, Huang Y, Wang S, Xu X. 2022. Safety criticism and ethical dilemma of autonomous vehicles. AI and Ethics 2:869−74

doi: 10.1007/s43681-021-00128-2
[69]

Cai Q, Abdel-Aty M, Yuan J, Lee J, Wu, Y. 2020. Real-time crash prediction on expressways using deep generative models. Transportation Research Part C: Emerging Technologies 117:102697

doi: 10.1016/j.trc.2020.102697
[70]

Kashifi MT, Al-Sghan IY, Rahman SM, Al-Ahmadi HM. 2022. Spatiotemporal grid-based crash prediction — application of a transparent deep hybrid modeling framework. Neural Computing and Applications 24:20655−69

doi: 10.1007/s00521-022-07511-y