| [1] |
World Health Organization. 2018. Global Status Report on Road Safety 2018. https://iris.who.int/handle/10665/276462 |
| [2] |
United Nations. 2020. Improving Global Road Safety: A/RES/74/299. General Assembly Resolution 74/299. https://undocs.org/en/A/RES/74/299 |
| [3] |
World Health Organization. 2021. Global Plan - Decade of Action for Road Safety 2021-2030. https://cdn.who.int/media/docs/default-source/documents/health-topics/road-traffic-injuries/global-plan-for-road-safety.pdf |
| [4] |
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 |
| [5] |
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 |
| [6] |
Yu R, Abdel-Aty M. 2013. Multi-level Bayesian analyses for single-and multi-vehicle freeway crashes. Accident Analysis & Prevention 58:97−105 doi: 10.1016/j.aap.2013.04.025 |
| [7] |
Formosa N, Quddus M, Ison S, Abdel-Aty M, Yuan J. 2020. Predicting real-time traffic conflicts using deep learning. Accident Analysis & Prevention 136:105429 doi: 10.1016/j.aap.2019.105429 |
| [8] |
Winlaw M, Steiner SH, MacKay RJ, Hilal AR. 2019. Using telematics data to find risky driver behaviour. Accident Analysis & Prevention 131:131−36 doi: 10.1016/j.aap.2019.06.003 |
| [9] |
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 |
| [10] |
Hossain MM, Rahman MA. 2023. Understanding the potential key risk factors associated with teen driver crashes in the United States: a literature review. Digital Transportation and Safety 2(4):268−77 doi: 10.48130/dts-2023-0022 |
| [11] |
Shi L, Qian C, Guo F. 2022. Real-time driving risk assessment using deep learning with XGBoost. Accident Analysis & Prevention 178:106836 doi: 10.1016/j.aap.2022.106836 |
| [12] |
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 |
| [13] |
Pande A, Das A, Abdel-Aty M, Hassan H. 2011. Estimation of real-time crash risk: are all freeways created equal? Transportation Research Record 2237(1):60−66 doi: 10.3141/2237-07 |
| [14] |
Shew C, Pande A, Nuworsoo C. 2013. Transferability and robustness of real-time freeway crash risk assessment. Journal of Safety Research 46:83−90 doi: 10.1016/j.jsr.2013.04.005 |
| [15] |
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 2432:91−98 doi: 10.3141/2432-11 |
| [16] |
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 1897:88−95 doi: 10.3141/1897-12 |
| [17] |
Abdel-Aty M, Uddin N, Pande A. 2005. Split models for predicting multivehicle crashes during high-speed and low-speed operating conditions on freeways. Transportation Research Record 1908:51−58 doi: 10.1177/0361198105190800107 |
| [18] |
Oh C, Oh JS, Ritchie SG. 2005. Real-time hazardous traffic condition warning system: Framework and evaluation. IEEE Transactions on Intelligent Transportation Systems 6(3):265−72 doi: 10.1109/TITS.2005.853693 |
| [19] |
Lee C, Abdel-Aty M. 2006. Temporal variations in traffic flow and ramp-related crash risk. Proc. Applications of Advanced Technology in Transportation, 13 Aug 2006, Chicago, Illinois, USA. Virginia, USA: American Society of Civil Engineers. pp. 244−49. doi: 10.1061/40799(213)40 |
| [20] |
Yang K, Wang X, Yu R. 2018. A Bayesian dynamic updating approach for urban expressway real-time crash risk evaluation. Transportation Research Part C: Emerging Technologies 96:192−207 doi: 10.1016/j.trc.2018.09.020 |
| [21] |
Zheng Z, Ahn S, Monsere CM. 2010. Impact of traffic oscillations on freeway crash occurrences. Accident Analysis & Prevention 42(2):626−36 doi: 10.1016/j.aap.2009.10.009 |
| [22] |
Xu C, Wang W, Liu P, Guo R, Li Z. 2014. Using the Bayesian updating approach to improve the spatial and temporal transferability of real-time crash risk prediction models. Transportation Research Part C: Emerging Technologies 38:167−76 doi: 10.1016/j.trc.2013.11.020 |
| [23] |
Wang L, Abdel-Aty M, Shi Q, Park J. 2015. Real-time crash prediction for expressway weaving segments. Transportation Research Part C: Emerging Technologies 61:1−10 doi: 10.1016/j.trc.2015.10.008 |
| [24] |
Pande A, Abdel-Aty M. 2006. Assessment of freeway traffic parameters leading to lane-change related collisions. Accident Analysis & Prevention 38(5):936−48 doi: 10.1016/j.aap.2006.03.004 |
| [25] |
Abdel-Aty M, Pande A, Das A, Knibbe WJ. 2008. Assessing safety on Dutch freeways with data from infrastructure-based intelligent transportation systems. Transportation Research Record 2083:153−61 doi: 10.3141/2083-18 |
| [26] |
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 |
| [27] |
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 |
| [28] |
Basso F, Basso LJ, Bravo F, Pezoa R. 2018. Real-time crash prediction in an urban expressway using disaggregated data. Transportation Research Part C: Emerging Technologies 86:202−19 doi: 10.1016/j.trc.2017.11.014 |
| [29] |
Abdel-Aty M, Pande A. 2005. Identifying crash propensity using specific traffic speed conditions. Journal of Safety Research 36(1):97−108 doi: 10.1016/j.jsr.2004.11.002 |
| [30] |
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 |
| [31] |
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 2673(4):314−26 doi: 10.1177/0361198119840611 |
| [32] |
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 & Prevention 162:106409 doi: 10.1016/j.aap.2021.106409 |
| [33] |
Lin L, Wang Q, Sadek AW. 2015. A novel variable selection method based on frequent pattern tree for real-time traffic accident risk prediction. Transportation Research Part C: Emerging Technologies 55:444−59 doi: 10.1016/j.trc.2015.03.015 |
| [34] |
Sun J, Sun J. 2016. Real-time crash prediction on urban expressways: identification of key variables and a hybrid support vector machine model. IET Intelligent Transport Systems 10(5):331−37 doi: 10.1049/iet-its.2014.0288 |
| [35] |
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 |
| [36] |
Zheng Y, Han L, Yu J, Yu R. 2023. Driving risk assessment under the connected vehicle environment: a CNN-LSTM modeling approach. Digital Transportation and Safety 2(3):211−19 doi: 10.48130/DTS-2023-0017 |
| [37] |
Yu R, Wang Y, Zou Z, Wang L. 2020. Convolutional neural networks with refined loss functions for the real-time crash risk analysis. Transportation Research Part C: Emerging Technologies 119:102740 doi: 10.1016/j.trc.2020.102740 |
| [38] |
Yang K, Quddus M, Antoniou C. 2022. Developing a new real-time traffic safety management framework for urban expressways utilizing reinforcement learning tree. Accident Analysis & Prevention 178:106848 doi: 10.1016/j.aap.2022.106848 |
| [39] |
Khondakar B, Sayed T, Lovegrove G. 2010. Transferability of community-based collision prediction models for use in road safety planning applications. Journal of Transportation Engineering 136(10):871−80 doi: 10.1061/(ASCE)TE.1943-5436.0000153 |
| [40] |
Srinivasan R, Colety M, Bahar G, Crowther B, Farmen M. 2016. Estimation of calibration functions for predicting crashes on rural two-lane roads in Arizona. Transportation Research Record 2583(1):17−24 doi: 10.3141/2583-03 |
| [41] |
Hadayeghi A, Shalaby AS, Persaud BN, Cheung C. 2006. Temporal transferability and updating of zonal level accident prediction models. Accident Analysis & Prevention 38(3):579−89 doi: 10.1016/j.aap.2005.12.003 |
| [42] |
Farid A, Abdel-Aty M, Lee J. 2018. Transferring and calibrating safety performance functions among multiple states. Accident Analysis & Prevention 117:276−87 doi: 10.1016/j.aap.2018.04.024 |
| [43] |
Farid A, Abdel-Aty M, Lee J. 2019. Comparative analysis of multiple techniques for developing and transferring safety performance functions. Accident Analysis & Prevention 122:85−98 doi: 10.1016/j.aap.2018.09.024 |
| [44] |
La Torre F, Meocci M, Domenichini L, Branzi V, Tanzi N, et al. 2019. Development of an accident prediction model for Italian freeways. Accident Analysis & Prevention 124:1−11 doi: 10.1016/j.aap.2018.12.023 |
| [45] |
Feng M, Wang X, Lee J, Abdel-Aty M, Mao S. 2020. Transferability of safety performance functions and hotspot identification for freeways of the United States and China. Accident Analysis & Prevention 139:105493 doi: 10.1016/j.aap.2020.105493 |
| [46] |
Tang D, Yang X, Wang X. 2020. Improving the transferability of the crash prediction model using the TrAdaBoost. R2 algorithm. Accident Analysis & Prevention 141:105551 doi: 10.1016/j.aap.2020.105551 |
| [47] |
Ahmed M, Abdel-Aty M. 2013. A data fusion framework for real-time risk assessment on freeways. Transportation Research Part C: Emerging Technologies 26:203−13 doi: 10.1016/j.trc.2012.09.002 |
| [48] |
Huang T, Wang S, Sharma A. 2020. Highway crash detection and risk estimation using deep learning. Accident Analysis & Prevention 135:105392 doi: 10.1016/j.aap.2019.105392 |
| [49] |
Finn C, Abbeel P, Levine S. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. arXiv 2017:Preprint doi: 10.48550/arXiv.1703.03400 |
| [50] |
Lu J, Gong P, Ye J, Zhang J, Zhang C. 2023. A survey on machine learning from few samples. Pattern Recognition 139:109480 doi: 10.1016/j.patcog.2023.109480 |
| [51] |
Wang Y, Yao Q, Kwok JT, Ni LM. 2020. Generalizing from a few examples: A survey on few-shot learning. ACM Computing Surveys 53(3):1−34 doi: 10.1145/3386252 |
| [52] |
Hospedales T, Antoniou A, Micaelli P, Storkey A. 2022. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(9):5149−69 doi: 10.1109/TPAMI.2021.3079209 |
| [53] |
Xu C, Wang W, Liu P. 2013. A genetic programming model for real-time crash prediction on freeways. IEEE Transactions on Intelligent Transportation Systems 14(2):574−86 doi: 10.1109/TITS.2012.2226240 |
| [54] |
Yu R, Abdel-Aty M. 2014. An optimal variable speed limits system to ameliorate traffic safety risk. Transportation Research Part C: Emerging Technologies 46:235−46 doi: 10.1016/j.trc.2014.05.016 |
| [55] |
Hanley JA, McNeil BJ. 1982. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1):29−36 doi: 10.1148/radiology.143.1.7063747 |
| [56] |
Morris C, Yang JJ. 2021. Effectiveness of resampling methods in coping with imbalanced crash data: Crash type analysis and predictive modeling. Accident Analysis & Prevention 159:106240 doi: 10.1016/j.aap.2021.106240 |
| [57] |
Ma Y, Zhang J, Lu J, Chen S, Xing G, et al. 2023. Prediction and analysis of likelihood of freeway crash occurrence considering risky driving behavior. Accident Analysis & Prevention 192:107244 doi: 10.1016/j.aap.2023.107244 |
| [58] |
Ali Y, Hussain F, Haque MM. 2024. Advances, challenges, and future research needs in machine learning-based crash prediction models: A systematic review. Accident Analysis & Prevention 194:107378 doi: 10.1016/j.aap.2023.107378 |
| [59] |
Baik S, Choi J, Kim H, Cho D, Min J, et al. 2021. Meta-learning with task-adaptive loss function for few-shot learning. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, 10−17 Oct. 2021. USA: IEEE. pp. 9465−74. doi: 10.1109/ICCV48922.2021.00933 |