Abstract
The topic of road accident severity has received considerable focus in recent years. Despite advancements in traffic safety management, the prevalence of road traffic casualties continues to be a cause for concern. The increasing popularity of deep learning methods has led to their application in understanding this phenomenon. However, effectively tuning these models to obtain satisfactory outcomes poses significant challenges. This study presents a hybrid system that combines a Convolution Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to predict road accident outcomes. The system incorporates a Genetic Algorithm for feature selection and optimizing hyperparameters. The model's results were compared to baseline learning classifiers. This study utilizes data on road accidents in the United Kingdom from 2000 to 2018. The study found that using a Genetic algorithm for feature selection and optimizing hyperparameters improved the performance of the model.
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References
WHO, 2018, “Global Status Report on Road Safety 2018”, Geneva (2018)
Sameen, M.I., Pradhan, B.: Assessment of the effects of expressway geometric design features on the frequency of accident crash rates using high-resolution laser scanning data and GIS” Geomatics. Nat. Hazards Risk 8(2), 733–747 (2016)
Rumar, K.: Transport safety visions, targets and strategies: Beyond 2000,” First Eur. Transp. Saf. Lecture, Brussels, Eur. Transp. Saf. Council, pp. 6–8 (1999)
Joshua, S.C., Garber, N.J.: Estimating truck accident rate and involvements using linear and Poisson regression models. Transport. Plann. Technol. 15(1), 41–58 (1990)
Shahsavari, S., Mohammadi, A., Mostafaei, S., et al.: Analysis of injuries and deaths from road traffic accidents in Iran: bivariate regression approach. BMC Emerg. Med. 22, 130 (2022). https://doi.org/10.1186/s12873-022-00686-6
Zhong, W., Du, L.: Predicting traffic casualties using support vector machines with heuristic algorithms: a study based on collision data of Urban Roads. Sustainability 15, 2944 (2023). https://doi.org/10.3390/su15042944
Sowdagur, J., Rozbully-Sowdagur, B., Suddul, G.: An Artificial Neural Network Approach for Road Accident Severity Prediction, pp. 267–270 (2022). https://doi.org/10.1109/ZINC55034.2022.9840576
Sowdagur, J.A., Rozbully-Sowdagur, B.T.B., Suddul, G.: An Artificial Neural Network Approach for Road Accident Severity Prediction. In: IEEE Zooming Innovation in Consumer Technologies Conference (ZINC), Novi Sad, Serbia, pp. 267–270 (2022). https://doi.org/10.1109/ZINC55034.2022.9840576
Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016)
Hamilton, B.A., Bakhit, P.R., Ishak, S.: An eXtreme gradient boosting method for identifying the factors contributing to crash/near-crash events: a naturalistic driving study.Can. J. Civ. Eng., 1–32 (2019)
Shapley, L.S.: A value for n-person games. Contrib. to Theory Games, pp. 307–317 (1953)
Parsa, A.B., Movahedi, A., Taghipour, H., Derrible, S., Mohammadian, A.: Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis. Accid Anal Prev, 136, Article 105405 (2020)
Aghaabbasi, M., Ali, M., Jasiński, M., Leonowicz, Z., Novák, T.: On hyperparameter optimization of machine learning methods using a bayesian optimization algorithm to predict work travel mode choice. IEEE Access 11, 19762–19774 (2023). https://doi.org/10.1109/ACCESS.2023.3247448
Infante, P., et al.: Comparison of statistical and machine-learning models on road traffic accident severity classification. Computers 11, 80 (2022). https://doi.org/10.3390/computers11050080
James, G., Witten, D., Hastie, T., Tibshirani, R.: An introduction to statistical learning. Springer (2013)
Katoch, S., Chauhan, S.S., Kumar, V.: A review on genetic algorithm: past, present, and future. Multimed Tools Appl 80, 8091–8126 (2021). https://doi.org/10.1007/s11042-020-10139-6
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We would like to express our sincere gratitude to the esteemed reviewers for their valuable time, insightful comments, and constructive feedback during the review process. Their expertise and commitment will greatly contribute to the enhancement of the quality and clarity of this manuscript. Your dedicated efforts will be instrumental in shaping and refining our work. We truly appreciate your professionalism and expertise, which will significantly strengthen the overall quality of the paper.
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Tabiti, A.e. (2024). Neuro-Genetic System: A Hybrid System of CNN-BiLSTM Optimized by Genetic Algorithm for Road Accident Severity Prediction. In: Cao, C., Chen, H., Zhao, L., Arshad, J., Asyhari, T., Wang, Y. (eds) Knowledge Science, Engineering and Management. KSEM 2024. Lecture Notes in Computer Science(), vol 14885. Springer, Singapore. https://doi.org/10.1007/978-981-97-5495-3_3
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DOI: https://doi.org/10.1007/978-981-97-5495-3_3
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