[1]

WHO. 2017. Road traffic injuries. www.who.int/en/news-room/fact-sheets/detail/road-traffic-injuries (accessed 2019 Jul 18)
[2]

Liu Q, Li C, Jiang H, Nie S, Chen L. 2022. Transfer learning-based highway crash risk evaluation considering manifold characteristics of traffic flow. Accident Analysis & Prevention 168:106598

doi: 10.1016/j.aap.2022.106598
[3]

Jiang F, Yuen KKR, Lee EWM. 2020. A long short-term memory-based framework for crash detection on freeways with traffic data of different temporal resolutions. Accident Analysis & Prevention 141:105520

doi: 10.1016/j.aap.2020.105520
[4]

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 54:176−186

doi: 10.1016/j.trc.2015.03.006
[5]

Abdel-Aty MA, Hassan HM, Ahmed M, Al-Ghamdi AS. 2012. Real-time prediction of visibility related crashes. Transportation Research Part C: Emerging Technologies 24:288−298

doi: 10.1016/j.trc.2012.04.001
[6]

Golob TF, Recker WW, Alvarez VM. 2004. Freeway safety as a function of traffic flow. Accident Analysis & Prevention 36:933−946

doi: 10.1016/j.aap.2003.09.006
[7]

Lee C, Hellinga B, Saccomanno F. 2003. Real-time crash prediction model for application to crash prevention in freeway traffic. Transportation Research Record: Journal of the Transportation Research Board 1840:67−77

doi: 10.3141/1840-08
[8]

Li Z, Wang W, Chen R, Liu P, Xu C. 2013. Evaluation of the impacts of speed variation on freeway traffic collisions in various traffic states. Traffic Injury Prevention 14:861−866

doi: 10.1080/15389588.2013.775433
[9]

Xu C, Liu P, Wang W, Li Z. 2012. Evaluation of the impacts of traffic states on crash risks on freeways. Accident Analysis & Prevention 47:162−171

doi: 10.1016/j.aap.2012.01.020
[10]

BDR. 2010. China mobile security market research report. www.sohu.com/a/358736654_783965
[11]

Mihajlovic V, Petkovic M. 2001. Dynamic Bayesian networks: a state of the art. CTIT Tech. reports Ser. 34. University of Twente, Enschede. pp. 1–37
[12]

Sammut C, Webb GI. 2017. Dynamic Bayesian network. In Encyclopedia of Machine Learning and Data Mining, eds Sammut C. Webb GI. Boston, MA: Springer US. pp. 377 doi: 10.1007/978-1-4899-7687-1_100125
[13]

Hou Q, Tarko AP, Meng X. 2018. Investigating factors of crash frequency with random effects and random parameters models: new insights from Chinese freeway study. Accident Analysis & Prevention 120:1−12

doi: 10.1016/j.aap.2018.07.010
[14]

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 & Prevention 111:56−62

doi: 10.1016/j.aap.2017.11.024
[15]

Ma Z, Zhang H, Chien SIJ, Wang J, Dong C. 2017. Predicting expressway crash frequency using a random effect negative binomial model: a case study in China. Accident Analysis & Prevention 98:214−222

doi: 10.1016/j.aap.2016.10.012
[16]

Zheng L, Sun J, Meng X. 2018. Crash prediction model for basic freeway segments incorporating influence of road geometrics and traffic signs. Journal of Transportation Engineering, Part A: Systems 144:04018030

doi: 10.1061/JTEPBS.0000155
[17]

Shi Q, Abdel-Aty M. 2015. Big Data applications in real-time traffic operation and safety monitoring and improvement on urban expressways. Transportation Research Part C: Emerging Technologies 58:380−394

doi: 10.1016/j.trc.2015.02.022
[18]

Pande A, Das A, Abdel-Aty M, Hassan H. 2011. Estimation of real-time crash risk: are all freeways created equal? Transportation Research Record: Journal of the Transportation Research Board 2237:60−66

doi: 10.3141/2237-07
[19]

Abdel-Aty MA, Pemmanaboina R. 2006. Calibrating a real-time traffic crash-prediction model using archived weather and ITS traffic data. IEEE Transactions on Intelligent Transportation Systems 7:167−174

doi: 10.1109/TITS.2006.874710
[20]

Lee C, Hellinga B, Saccomanno F. 2003. Proactive freeway crash prevention using real-time traffic control. Canadian Journal of Civil Engineering 30:1034−1041

doi: 10.1139/l03-040
[21]

Xu C, Wang W, Liu P. 2013. Identifying crash-prone traffic conditions under different weather on freeways. Journal of Safety Research 46:135−144

doi: 10.1016/j.jsr.2013.04.007
[22]

Parsa AB, Movahedi A, Taghipour H, Derrible S, Mohammadian AK. 2020. Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis. Accident Analysis & Prevention 136:105405

doi: 10.1016/j.aap.2019.105405
[23]

Sun J, Sun J, Chen P. 2014. Use of support vector machine models for real-time prediction of crash risk on urban expressways. Transportation Research Record: Journal of the Transportation Research Board 2432:91−98

doi: 10.3141/2432-11
[24]

Xu C, Wang W, Liu P, Zhang F. 2015. Development of a real-time crash risk prediction model incorporating the various crash mechanisms across different traffic states. Traffic Injury Prevention 16:28−35

doi: 10.1080/15389588.2014.909036
[25]

Yuan J, Abdel-Aty M, Gong Y, Cai Q. 2019. Real-time crash risk prediction using long short-term memory recurrent neural network. Transportation Research Record: Journal of the Transportation Research Board 2673:314−326

doi: 10.1177/0361198119840611
[26]

Yang Y, He K, Wang YP, Yuan ZZ, Yin YH, et al. 2022. Identification of dynamic traffic crash risk for cross-area freeways based on statistical and machine learning methods. Physica A: Statistical Mechanics and Its Applications 595:127083

doi: 10.1016/j.physa.2022.127083
[27]

Kidando E, Kitali AE, Kutela B, Ghorbanzadeh M, Karaer A, et al. 2021. Prediction of vehicle occupants injury at signalized intersections using real-time traffic and signal data. Accident Analysis & Prevention 149:105869

doi: 10.1016/j.aap.2020.105869
[28]

Abdel-Aty M, Uddin N, Pande A, Abdalla MF, Hsia L. 2004. Predicting freeway crashes from loop detector data by matched case-control logistic regression. Transportation Research Record: Journal of the Transportation Research Board 1897:88−95

doi: 10.3141/1897-12
[29]

Liu M, Chen Y. 2017. Predicting real-time crash risk for urban expressways in China. Mathematical Problems in Engineering 2017:6263726

doi: 10.1155/2017/6263726
[30]

Wang J, Song H, Fu T, Behan M, Jie L, et al. 2022. Crash prediction for freeway work zones in real time: a comparison between Convolutional Neural Network and Binary Logistic Regression model. International Journal of Transportation Science and Technology 11:484−495

doi: 10.1016/j.ijtst.2021.06.002
[31]

Li X, Lord D, Zhang Y, Xie Y. 2008. Predicting motor vehicle crashes using Support Vector Machine models. Accident Analysis & Prevention 40:1611−1618

doi: 10.1016/j.aap.2008.04.010
[32]

Zeng Q, Huang H, Pei X, Wong SC, Gao M. 2016. Rule extraction from an optimized neural network for traffic crash frequency modeling. Accident Analysis & Prevention 97:87−95

doi: 10.1016/j.aap.2016.08.017
[33]

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
[34]

Guo M, Zhao X, Yao Y, Bi C, Su Y. 2022. Application of risky driving behavior in crash detection and analysis. Physica A: Statistical Mechanics and Its Applications 591:126808

doi: 10.1016/j.physa.2021.126808
[35]

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
[36]

Yang Y, Wu Y. 2014. VE dimension induced by Bayesian networks over the boolean domain. Pattern Analysis and Applications 17:799−807

doi: 10.1007/s10044-013-0320-3
[37]

Yang Y, Wu Y. 2012. On the properties of concept classes induced by multivalued Bayesian networks. Information Sciences 184:155−165

doi: 10.1016/j.ins.2011.08.031
[38]

Yang Y, Wu Y. 2009. Inner product space and concept classes induced by Bayesian networks. Acta Applicandae Mathematicae 106:337−348

doi: 10.1007/s10440-008-9301-8
[39]

Yang Y, Wu Y. 2009. VC dimension and inner product space induced by Bayesian networks. International Journal of Approximate Reasoning 50:1036−1045

doi: 10.1016/j.ijar.2009.04.001
[40]

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 45:373−381

doi: 10.1016/j.aap.2011.08.004
[41]

Roy A, Hossain M, Muromachi Y. 2018. Enhancing the prediction performance of real-time crash prediction models: a cell transmission-dynamic bayesian network approach. Transportation Research Record: Journal of the Transportation Research Board 2672:58−68

doi: 10.1177/0361198118797802
[42]

Roy A, Kobayashi R, Hossain M, Muromachi Y. 2016. Real-time crash prediction model for urban expressway using dynamic Bayesian network. Journal of Japan Society of Civil Engineers Ser. D3 72:I_1331−I_1338

doi: 10.2208/jscejipm.72.I_1331
[43]

Wu M, Shan D, Wang Z, Sun X, Liu J, et al. 2019. A Bayesian network model for real-time crash prediction based on selected variables by random forest. Proc. 2019 5th International Conference on Transportation Information and Safety (ICTIS), Liverpool, UK, 2019. Liverpool, UK: IEEE. pp. 670–677 doi: 10.1109/ICTIS.2019.8883694
[44]

Fan S, Blanco-Davis E, Yang Z, Zhang J, Yan X. 2020. Incorporation of human factors into maritime accident analysis using a data-driven Bayesian network. Reliability Engineering & System Safety 203:107070

doi: 10.1016/j.ress.2020.107070
[45]

Murphy KP. 2001. The Bayes net toolbox for matlab. Computing Science and Statistics 33:1024−1034
[46]

Dempster AP, Laird NM, Rubin DB. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological) 39:1−22

doi: 10.1111/j.2517-6161.1977.tb01600.x
[47]

Yu R, Wang X, Abdel-Aty M. 2017. A hybrid latent class analysis modeling approach to analyze urban expressway crash risk. Accident Analysis & Prevention 101:37−43

doi: 10.1016/j.aap.2017.02.002
[48]

Dutta N, Fontaine MD. 2019. Improving freeway segment crash prediction models by including disaggregate speed data from different sources. Accident Analysis & Prevention 132:105253

doi: 10.1016/j.aap.2019.07.029
[49]

Hossain M, Muromachi Y. 2010. Development of a real-time crash prediction model for urban expressway. Journal of the Eastern Asia Society for Transportation Studies 8:2092−2107

doi: 10.11175/easts.8.2092
[50]

Stipancic J, Miranda-Moreno L, Saunier N, Labbe A. 2019. Network screening for large urban road networks: using GPS data and surrogate measures to model crash frequency and severity. Accident Analysis & Prevention 125:290−301

doi: 10.1016/j.aap.2019.02.016