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Machine Learning Techniques for Fatal Accident Prediction


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Alkheder, S., Taamneh, M., & Taamneh, S. (2017). Severity Prediction of Traffic Accident Using an Artificial Neural Network. Journal of Forecasting, 36(1), 100–108. https://doi.org/10.1002/for.2425 Search in Google Scholar

Augé, C. G., & Navarro, S. C. i. (2022). Fatal accidents in Catalonia between 2014 and 2022. https://zenodo.org/records/7316989/files/AccidentsMortals_2014-2022_Catalunya.csv?download=1 Search in Google Scholar

Basagaña, X., & de la Peña-Ramirez, C. (2023). Ambient temperature and risk of motor vehicle crashes: A countrywide analysis in Spain. Environmental Research, 216(October 2022). https://doi.org/10.1016/j.envres.2022.114599 Search in Google Scholar

Behzadi Goodari, M., Sharifi, H., Dehesh, P., Mosleh-Shirazi, M. A., & Dehesh, T. (2023). Factors affecting the number of road traffic accidents in Kerman province, southeastern Iran (2015–2021). Scientific Reports, 13(1), 1–9. https://doi.org/10.1038/s41598-023-33571-8 Search in Google Scholar

Beirigo, B. A., Schulte, F., & Negenborn, R. R. (2018). Integrating People and Freight Transportation Using Shared Autonomous Vehicles with Compartments. IFACPapersOnLine, 51(9), 392–397. https://doi.org/10.1016/j.ifacol.2018.07.064 Search in Google Scholar

Bridgelall, R., & Tolliver, D. D. (2024). Railroad accident analysis by machine learning and natural language processing. Journal of Rail Transport Planning and Management, 29(December 2023), 100429. https://doi.org/10.1016/j.jrtpm.2023.100429 Search in Google Scholar

Castro, Y., & Kim, Y. J. (2016). Data mining on road safety: Factor assessment on vehicle accidents using classification models. International Journal of Crashworthiness, 21(2), 104–111. https://doi.org/10.1080/13588265.2015.1122278 Search in Google Scholar

Catalan Traffic Service. (2024). Comunicats daccidents mortals. https://transit.gencat.cat/ca/el_servei/premsa_i_comunicacio/comunicats_d_accidents_mortals/ Search in Google Scholar

Chang, L. Y., & Chien, J. T. (2013). Analysis of driver injury severity in truck-involved accidents using a non-parametric classification tree model. Safety Science, 51(1), 17–22. https://doi.org/10.1016/j.ssci.2012.06.017 Search in Google Scholar

Comi, A., Polimeni, A., & Balsamo, C. (2022). Road Accident Analysis with Data Mining Approach: Evidence from Rome. Transportation Research Procedia, 62(Ewgt 2021), 798–805. https://doi.org/10.1016/j.trpro.2022.02.099 Search in Google Scholar

de Oña, J., de Oña, R., Eboli, L., Forciniti, C., Machado, J. L., & Mazzulla, G. (2014). Analysing the Relationship Among Accident Severity, Drivers’ Behaviour and Their Socio-economic Characteristics in Different Territorial Contexts. Procedia - Social and Behavioral Sciences, 160(Cit), 74–83. https://doi.org/10.1016/j.sbspro.2014.12.118 Search in Google Scholar

Delen, D., Sharda, R., & Bessonov, M. (2006). Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks. Accident Analysis and Prevention, 38(3), 434–444. https://doi.org/10.1016/j.aap.2005.06.024 Search in Google Scholar

Duarte Monedero, B., A. Gil-Alana, L., & Valbuena Martínez, M. C. (2021). Road accidents in Spain: Are they persistent? IATSS Research, 45(3), 317–325. https://doi.org/10.1016/j.iatssr.2021.01.002 Search in Google Scholar

Gatera, A., Kuradusenge, M., Bajpai, G., Mikeka, C., & Shrivastava, S. (2023). Comparison of random forest and support vector machine regression models for forecasting road accidents. Scientific African, 21, e01739. https://doi.org/10.1016/j.sciaf.2023.e01739 Search in Google Scholar

Hashmienejad, S. H.-A., & Hasheminejad, S. M. H. (2017). Traffic accident severity prediction using a novel multi-objective genetic algorithm. International Journal of Crashworthiness, 22(4), 425–440. https://doi.org/10.1080/13588265.2016.1275431 Search in Google Scholar

Hu, N., Zhang, D., Xie, K., Liang, W., & Hsieh, M. Y. (2022). Graph learning-based spatial-temporal graph convolutional neural networks for traffic forecasting. Connection Science, 34(1), 429–448. https://doi.org/10.1080/09540091.2021.2006607 Search in Google Scholar

Insurance Institute for Highway Safety. (2024). Fatality Facts 2021 Yearly snapshot. U.S. Department of Transportation’s. https://www.iihs.org/topics/fatality-statistics/detail/yearly-snapshot Search in Google Scholar

Kang, K., & Ryu, H. (2019). Predicting types of occupational accidents at construction sites in Korea using random forest model. Safety Science, 120(June), 226–236. https://doi.org/10.1016/j.ssci.2019.06.034 Search in Google Scholar

Kaplan, S., & Prato, C. G. (2012). Risk factors associated with bus accident severity in the United States: A generalized ordered logit model. Journal of Safety Research, 43(3), 171–180. https://doi.org/10.1016/j.jsr.2012.05.003 Search in Google Scholar

Kashyap, A. A., Raviraj, S., Devarakonda, A., Nayak K, S. R., Santhosh, K. V., & Bhat, S. J. (2022). Traffic flow prediction models–A review of deep learning techniques. Cogent Engineering, 9(1). https://doi.org/10.1080/23311916.2021.2010510 Search in Google Scholar

Kaye, S. A., Lewis, I., Forward, S., & Delhomme, P. (2020). A priori acceptance of highly automated cars in Australia, France, and Sweden: A theoretically-informed investigation guided by the TPB and UTAUT. Accident Analysis and Prevention, 137(May 2019), 105441. https://doi.org/10.1016/j.aap.2020.105441 Search in Google Scholar

Kunt, M. M., Aghayan, I., & Noii, N. (2011). Prediction for traffic accident severity: Comparing the artificial neural network, genetic algorithm, combined genetic algorithm and pattern search methods. Transport, 26(4), 353–366. https://doi.org/10.3846/16484142.2011.635465 Search in Google Scholar

Lee, H., Kang, M., Hwang, K., & Yoon, Y. (2024). Heliyon The typical AV accident scenarios in the urban area obtained by clustering and association rule mining of real-world accident reports. Heliyon, 10(3), e25000. https://doi.org/10.1016/j.heliyon.2024.e25000 Search in Google Scholar

Li, R., Pereira, F. C., & Ben-Akiva, M. E. (2018). Overview of traffic incident duration analysis and prediction. European Transport Research Review, 10(2), 1–13. https://doi.org/10.1186/s12544-018-0300-1 Search in Google Scholar

Moghaddam, F. R., Afandizadeh, S., & Ziyadi, M. (2011). Prediction of accident severity using artificial neural networks. International Journal of Civil Engineering, 9(1), 41–49. Search in Google Scholar

Mohamed, M. G., Saunier, N., Miranda-Moreno, L. F., & Ukkusuri, S. V. (2013). A clustering regression approach: A comprehensive injury severity analysis of pedestrian-vehicle crashes in New York, US and Montreal, Canada. Safety Science, 54, 27–37. https://doi.org/10.1016/j.ssci.2012.11.001 Search in Google Scholar

Moriano, P., Berres, A., Xu, H., & Sanyal, J. (2024). Spatiotemporal features of traffic help reduce automatic accident detection time. Expert Systems with Applications, 244(November 2023), 122813. https://doi.org/10.1016/j.eswa.2023.122813 Search in Google Scholar

Noy, I. Y., Shinar, D., & Horrey, W. J. (2018). Automated driving: Safety blind spots. Safety Science, 102(March 2017), 68–78. https://doi.org/10.1016/j.ssci.2017.07.018 Search in Google Scholar

Papadoulis, A., Quddus, M., & Imprialou, M. (2019). Evaluating the safety impact of connected and autonomous vehicles on motorways. Accident Analysis and Prevention, 124(December 2018), 12–22. https://doi.org/10.1016/j.aap.2018.12.019 Search in Google Scholar

Rezaei, A., & Caulfield, B. (2020). Examining public acceptance of autonomous mobility. Travel Behaviour and Society, 21(November 2019), 235–246. https://doi.org/10.1016/j.tbs.2020.07.002 Search in Google Scholar

Rezapour, M., Mehrara Molan, A., & Ksaibati, K. (2020). Analyzing injury severity of motorcycle at-fault crashes using machine learning techniques, decision tree and logistic regression models. International Journal of Transportation Science and Technology, 9(2), 89–99. https://doi.org/10.1016/j.ijtst.2019.10.002 Search in Google Scholar

Rubio-Romero, J. C., Carmen Rubio Gámez, M., & Carrillo-Castrillo, J. A. (2013). Analysis of the safety conditions of scaffolding on construction sites. Safety Science, 55, 160–164. https://doi.org/10.1016/j.ssci.2013.01.006 Search in Google Scholar

Schoettle, B., & Sivak, M. (2014). Public Opinion About Self-Driving Vehicles in China, India, Japan, The U.S., The U.K., and Australia. In UMTRI (UMTRI-2014-3). The University of Michigan Transportation Research Institute, Ann Arbor, Michigan 48109-2150, U.S.A. Search in Google Scholar

Taamneh, M., Alkheder, S., & Taamneh, S. (2017). Data-mining techniques for traffic accident modeling and prediction in the United Arab Emirates. Journal of Transportation Safety and Security, 9(2), 146–166. https://doi.org/10.1080/19439962.2016.1152338 Search in Google Scholar

Toronto Police Service. (2022). Fatal Collisions: Toronto Police Service Public Safety Data Portal. https://data.torontopolice.on.ca/pages/fatalities Search in Google Scholar

WHO. (2018). Global status report on road safety 2018. Licence: CC BYNC- SA 3.0 IGO. https://www.who.int/publications/i/item/9789241565684 Search in Google Scholar

WHO. (2021). Global Plan Decade of actıon for road safety 2021-2030. https://www.who.int/publications/m/item/global-plan-for-the-decade-of-action-for-road-safety-2021-2030 Search in Google Scholar

WHO. (2024). WHO: Death on the roads. Global Status Report on Road Safety. https://extranet.who.int/roadsafety/death-on-the-roads/%0Ahttps://extranet.who.int/roadsafety/death-on-the-roads/#trends Search in Google Scholar

WHO Regional Office for Europe. (2009). European status report on road safety: towards safer roads and healthier transport choices. WHO Regional Office for Europe, Copenhagen. https://iris.who.int/handle/10665/107266 Search in Google Scholar

Yan, X., Radwan, E., & Abdel-Aty, M. (2005). Characteristics of rear-end accidents at signalized intersections using multiple logistic regression model. Accident Analysis and Prevention, 37(6), 983–995. https://doi.org/10.1016/j.aap.2005.05.001 Search in Google Scholar

Yannis, G., Dragomanovits, A., Laiou, A., La Torre, F., Domenichini, L., Richter, T., Ruhl, S., Graham, D., & Karathodorou, N. (2017). Road traffic accident prediction modelling: a literature review. Proceedings of the Institution of Civil Engineers: Transport, 170(5), 245–254. https://doi.org/10.1680/jtran.16.00067 Search in Google Scholar

Yokoyama, A., & Yamaguchi, N. (2020). Comparison between ANN and random forest for leakage current alarm prediction. Energy Reports, 6, 150–157. https://doi.org/10.1016/j.egyr.2020.11.271 Search in Google Scholar

Zermane, A., Mohd Tohir, M. Z., Zermane, H., Baharudin, M. R., & Mohamed Yusoff, H. (2023). Predicting fatal fall from heights accidents using random forest classification machine learning model. Safety Science, 159(November 2022), 106023. https://doi.org/10.1016/j.ssci.2022.106023 Search in Google Scholar

Zermane, H., & Drardja, A. (2022). Development of an efficient cement production monitoring system based on the improved random forest algorithm. International Journal of Advanced Manufacturing Technology, 120(3–4), 1853–1866. https://doi.org/10.1007/s00170-022-08884-z Search in Google Scholar

Zheng, M., Li, T., Zhu, R., Chen, J., Ma, Z., Tang, M., Cui, Z., & Wang, Z. (2019). Traffic accident’s severity prediction: A deep-learning approach-based CNN network. IEEE Access, 7, 39897–39910. https://doi.org/10.1109/ACCESS.2019.2903319 Search in Google Scholar

Zhou, X., Lu, P., Zheng, Z., Tolliver, D., & Keramati, A. (2020). Accident Prediction Accuracy Assessment for Highway-Rail Grade Crossings Using Random Forest Algorithm Compared with Decision Tree. Reliability Engineering and System Safety, 200, 106931. https://doi.org/10.1016/j.ress.2020.106931 Search in Google Scholar

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