A LITERATURE SURVEY OF CRASH INJURY SEVERITY PREDICTION
Keywords:
Traffic accidents, Road traffic injuries, Prediction modelsAbstract
Accurate prediction of the severity of road traffic accident injuries provides a foundation for accident prevention, emergency resource allocation, and response planning. As a result, the selection of traffic accident injury severity prediction models has garnered significant attention from both authorities and researchers. This paper aims to provide a comprehensive review of the research progress on traffic accident severity prediction models. It begins by reviewing relevant datasets and features used in severity prediction. Next, it summarizes various approaches and models in traffic accident severity prediction, including traditional statistical models and machine learning techniques. A comparative analysis of these models is then conducted. Finally, the paper discusses the key challenges in current research and explores future development trends, offering theoretical guidance for both researchers and practitioners.References
[1] World Health Organization. World health statistics 2024: Monitoring health for the SDGs, Sustainable Development Goals. World Health Organization, Geneva, 2024. https://www.who.int/data/gho/publications/worldhealth-statistics.
[2] Abdelwahab HT, Abdel-Aty MA. Development of artificial neural network models to predict driver injury severity in traffic accidents at signalized intersections. Transportation Research Record, 2001, 1746(1): 6-13.
[3] Ahmadi A, Jahangiri A, Berardi V, et al. Crash severity analysis of rear-end crashes in California using statistical and machine learning classification methods. Journal of Transportation Safety & Security, 2020, 12(4): 522-546.
[4] Vajari MA, Aghabayk K, Sadeghian M, et al. A multinomial logit model of motorcycle crash severity at Australian intersections. Journal of Safety Research, 2024, 73: 17-24.
[5] Acevedo MA, Corrada-Bravo CJ, Corrada-Bravo H, et al. Automated classification of bird and amphibian calls using machine learning: A comparison of methods. Ecological Informatics, 2009, 4(4): 206-214.
[6] Al-Moqri T, Haijun X, Namahoro JP, et al. Exploiting machine learning algorithms for predicting crash injury severity in Yemen: Hospital case study. Applied and Computational Mathematics, 2020, 9(5): 155-164.
[7] Arhin SA, Gatiba A. Predicting crash injury severity at unsignalized intersections using support vector machines and na?ve Bayes classifiers. Transportation Safety and Environment, 2020, 2(2): 120-132.
[8] Theofilatos A, Chen C, Antoniou C. Comparing machine learning and deep learning methods for real-time crash prediction. Transportation Research Record, 2019, 2673(8): 169-178.
[9] Khalesian M, Furno A, Leclercq L. Improving deep-learning methods for area-based traffic demand prediction via hierarchical reconciliation. Transportation Research Part C: Emerging Technologies, 2024, 159: 104410.
[10] Wu Z. Deep learning with improved metaheuristic optimization for traffic flow prediction. Journal of Computer Science and Technology Studies, 2024, 6(4): 47-53.
[11] Li Y, Li P, Yan D, et al. Deep knowledge distillation: A self-mutual learning framework for traffic prediction. Expert Systems with Applications, 2024, 252: 124138.
[12] Jian F, Tian L, Yuqiang Q. Traffic accident prediction method based on multi-view spatial-temporal learning. Bulletin of the Polish Academy of Sciences: Technical Sciences. 2024, 72(6).
[13] Alhaek F, Li T, Rajeh TM, et al. Encoding global semantic and localized geographic spatial-temporal relations for traffic accident risk prediction. 2024.
[14] Gao X, Haworth J, Ilyankou I, et al. Sma-hyper: Spatiotemporal multi-view fusion hypergraph learning for traffic accident prediction. 2024. arXiv preprint arXiv:2407.17642. 2024.
[15] Trirat P, Yoon S, Lee J-G. Mg-tar: Multi-view graph convolutional networks for traffic accident risk prediction. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(4): 3779-3794.
[16] Zhang D, Li J. Multi-view fusion neural network for traffic demand prediction. Information Sciences, 2023, 646: 119303.
[17] Ministry of Justice of the People's Republic of China. Standards for the Identification of the Degree of Human Injury. Tech. Rep. Announcement, Supreme People's Court Supreme People's Procuratorate Ministry of Public Security Ministry of State Security Ministry of Justice, Beijing. 2013. https://www.moj.gov.cn/pub/sfbgw/zwxxgk/fdzdgknr/fdzdgknrtzwj/201908/t20190816_207509.html.
[18] Kashani AT, Mohaymany AS. Analysis of the traffic injury severity on two-lane, two-way rural roads based on classification tree models. Safety Science, 2011, 49(10): 1314-1320.
[19] De O?a J, Mujalli RO, Calvo FJ. Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks. Accident Analysis & Prevention, 2011, 43(1): 402-411.
[20] Haq MT, Zlatkovic M, Ksaibati K. Assessment of commercial truck driver injury severity based on truck configuration along a mountainous roadway using hierarchical Bayesian random intercept approach. Accident Analysis & Prevention, 2021, 162: 106392.
[21] Wang B, Lin Y, Guo S, et al. Gsnet: Learning spatial-temporal correlations from geographical and semantic aspects for traffic accident risk forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2021, 35: 4402-4409.
[22] Li Y, Yu R, Shahabi C, et al. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. 2017. arXiv preprint arXiv:1707.01926. 2017.
[23] Guo S, Lin Y, Wan H, et al. Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting. IEEE Transactions on Knowledge and Data Engineering, 2021, 34(11): 5415-5428.
[24] Xu Z, Lv Z, Li J, et al. A novel perspective on travel demand prediction considering natural environmental and socioeconomic factors. IEEE Intelligent Transportation Systems Magazine, 2022, 15(1): 136-159.
[25] Oeschger G, Carroll P, Caulfield B. Micromobility and public transport integration: The current state of knowledge. Transportation Research Part D: Transport and Environment, 2020, 89: 102628.
[26] Wu A, Nowozin S, Meeds E, et al. Deterministic variational inference for robust Bayesian neural networks. 2018. arXiv preprint arXiv:1810.03958. 2018.
[27] Selvaraj S, Ramani G. Classification of seating position specific patterns in road traffic accident data through data mining techniques. In: Second International Conference on Computer Applications, 2012.
[28] Wang J, Kong Y, Fu T. Expressway crash risk prediction using back propagation neural network: A brief investigation