| [1] |
World Health Organization. 2023. Road traffic injuries. www.who.int/zh/news-room/fact-sheets/detail/road-traffic-injuries (Retrieved December 21, 2024) |
| [2] |
Ghosh B, Smith DP. 2014. Customization of automatic incident detection algorithms for signalized urban arterials. |
| [3] |
Li L, Qin L, Qu X, Zhang J, Wang Y, et al. 2019. Day-ahead traffic flow forecasting based on a deep belief network optimized by the multi-objective particle swarm algorithm. |
| [4] |
Al Kuwaiti A, Nazer K, Al-Reedy A, Al-Shehri S, Al-Muhanna A, et al. 2023. A review of the role of artificial intelligence in healthcare. |
| [5] |
Wang H, Fu T, Du Y, Gao W, Huang K, et al. 2023. Scientific discovery in the age of artificial intelligence. |
| [6] |
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. |
| [7] |
Abdel-Aty M, Uddin N, Pande A. 2005. Split models for predicting multivehicle crashes during high-speed and low-speed operating conditions on freeways. |
| [8] |
Oh C, Oh JS, Ritchie SG. 2005. Real-time hazardous traffic condition warning system: Framework and evaluation. |
| [9] |
Lee C, Abdel-Aty M. 2006. Temporal variations in traffic flow and ramp-related crash risk. In Applications of Advanced Technology in Transportation. Chicago: American Society of Civil Engineers. pp. 244–49 doi: 10.1061/40799(213)40 |
| [10] |
Yu R, Abdel-Aty M. 2013. Multi-level Bayesian analyses for single-and multi-vehicle freeway crashes. |
| [11] |
Yang K, Wang X, Yu R. 2018. A Bayesian dynamic updating approach for urban expressway real-time crash risk evaluation. |
| [12] |
Huang T, Wang S, Sharma A. 2020. Highway crash detection and risk estimation using deep learning. |
| [13] |
Pourroostaei Ardakani S, Liang X, Mengistu KT, So RS, Wei X, et al. 2023. Road car accident prediction using a machine-learning-enabled data analysis. |
| [14] |
Yu L, Du B, Hu X, Sun L, Han L, et al. 2021. Deep spatio-temporal graph convolutional network for traffic accident prediction. |
| [15] |
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. |
| [16] |
Zhang S, Abdel-Aty M. 2022. Real-time crash potential prediction on freeways using connected vehicle data. |
| [17] |
Yuan C, Li Y, Huang H, Wang S, Sun Z, et al. 2022. Using traffic flow characteristics to predict real-time conflict risk: a novel method for trajectory data analysis. |
| [18] |
Payne HJ, Tignor SC. 1978. Freeway incident-detection algorithms based on decision trees with states. Transportation Research Record 1978(682):30−37 |
| [19] |
Parkany E, Xie C. 2005. A complete review of incident detection algorithms & their deployment: what works and what doesn't. Technical Report NETCR37. New England Transportation Consortium, USA. https://onlinepubs.trb.org/onlinepubs/trispdfs/00988875.pdf |
| [20] |
Jin X, Srinivasan D, Cheu RL. 2001. Classification of freeway traffic patterns for incident detection using constructive probabilistic neural networks. |
| [21] |
Dogru N, Subasi A. 2018. Traffic accident detection using random forest classifier. 2018 15th Learning and Technology Conference (L&T), Jeddah, Saudi Arabia, 25–26 February 2018. USA: IEEE. pp. 40–45 doi: 10.1109/LT.2018.8368509 |
| [22] |
White J, Thompson C, Turner H, Dougherty B, Schmidt DC. 2011. WreckWatch: Automatic traffic accident detection and notification with smartphones. |
| [23] |
Ozbayoglu M, Kucukayan G, Dogdu E. 2016. A real-time autonomous highway accident detection model based on big data processing and computational intelligence. 2016 IEEE International Conference on Big Data (Big Data), Washington, DC, USA, 5–8 December 2016. USA: IEEE. pp. 1807–13 doi: 10.1109/BigData.2016.7840798 |
| [24] |
Gu Y, Qian Z, Chen F. 2016. From Twitter to detector: Real-time traffic incident detection using social media data. |
| [25] |
Mehrannia P, Bagi SSG, Moshiri B, Al‐Basir OA. 2023. Deep representation of imbalanced spatio-temporal traffic flow data for traffic accident detection. |
| [26] |
Li L, Lin Y, Du B, Yang F, Ran B. 2022. Real-time traffic incident detection based on a hybrid deep learning model. |
| [27] |
Xie T, Shang Q, Yu Y. 2022. Automated traffic incident detection: Coping with imbalanced and small datasets. |
| [28] |
Jilani U, Asif M, Rashid M, Siddique AA, Talha SMU, et al. 2022. Traffic congestion classification using GAN-based synthetic data augmentation and a novel 5-layer convolutional neural network model. |
| [29] |
Huang Y, Wei W, Yang H, Wu Q, Xu K. 2023. Intelligent algorithms for incident detection and management in smart transportation systems. |
| [30] |
Dabboussi AH, Jammal M. 2024. Traffic data augmentation using GANs for ITS. 2024 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT), Abu Dhabi, United Arab Emirates, 29 April 2024−01 May 2024. USA: IEEE. pp. 66-73 doi: 10.1109/DCOSS-IoT61029.2024.00020 |
| [31] |
Benabdallah Benarmas R, Beghdad Bey K. 2024. Improving road traffic speed prediction using data augmentation: a deep generative models-based approach. |
| [32] |
Liao C, Chen XM. 2024. A meta-learning approach to improving transferability for freeway traffic crash risk prediction. |
| [33] |
Avila A M, Mezić I. 2020. Data-driven analysis and forecasting of highway traffic dynamics. |
| [34] |
Qu Q, Shen Y, Yang M, Zhang R. 2024. Towards efficient traffic crash detection based on macro and micro data fusion on expressways: a digital twin framework. |
| [35] |
Yang K, Quddus M, Antoniou C. 2022. Developing a new real-time traffic safety management framework for urban expressways utilizing reinforcement learning tree. |
| [36] |
Zaitouny A, Fragkou AD, Stemler T, Walker DM, Sun Y, et al. 2022. Multiple sensors data integration for traffic incident detection using the quadrant scan. |
| [37] |
German Aerospace Center. n.d. SUMO at a glance. SUMO Documentation. https://sumo.dlr.de/docs/SUMO_at_a_Glance.html (Retrieved December 21, 2024) |
| [38] |
Krajzewicz D, Brockfeld E, Mikat J, Ringel J, Rössel C, et al. 2005. Simulation of modern traffic lights control systems using the open source traffic simulation SUMO. Proceedings of the 3rd Industrial Simulation Conference 2005. Berlin, Germany: EUROSIS-ETI. pp. 299–302 https://elib.dlr.de/21012 |
| [39] |
Koh SS, Zhou B, Yang P, Yang Z, Fang H, et al. 2018. Reinforcement learning for vehicle route optimization in SUMO. 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), Exeter, UK, 28–30 June 2018. USA: IEEE. pp. 1468–73 doi: 10.1109/HPCC/SmartCity/DSS.2018.00242 |
| [40] |
Kastner KH, Pau P. 2015. Experiences with SUMO in a real-life traffic monitoring system. SUMO 2015–Intermodal Simulation for Intermodal Transport 28, Berlin. pp. 1–10 www.researchgate.net/publication/291339917 |
| [41] |
Fernandes P, Nunes U. 2010. Platooning of autonomous vehicles with intervehicle communications in SUMO traffic simulator. 13th International IEEE Conference on Intelligent Transportation Systems, Funchal, Portugal, 19–22 September 2010. USA: IEEE. pp. 1313–18 doi: 10.1109/ITSC.2010.5625277 |
| [42] |
Shamsashtiany R, Ameri M. 2018. Road accidents prediction with multilayer perceptron (MLP) modelling case study: roads of Qazvin, Zanjan and Hamadan. |