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Deep learning model of convolutional neural networks powered by a genetic algorithm for prevention of traffic accidents severity

Author

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  • Pérez-Sala, Luis
  • Curado, Manuel
  • Tortosa, Leandro
  • Vicent, Jose F.

Abstract

The World Health Organization highlights that the number of annual road traffic deaths has reached 1.35 million (Global Status Report on Road Safety 2018). In addition, million of people suffer more or less important injuries as a consequence of this type of accidents. In this scenario, the prediction of the severity of traffic accidents is an essential point when it comes to improving the prevention and reaction of the entities responsible. On the other hand, the development of reliable methodologies to predict and classify the level of severity of traffic accidents, based on various variables, is a key component in the field of research in road safety. This work aims to propose a new approach, based on convolutional neural networks, for the detection of the severity of traffic accidents. Behind this objective is the preprocessing, analysis and visualization of data as well as the design, implementation and comparison of machine learning models considering accuracy as a performance indicator. For this purpose, a scalable and easily reusable methodology has been implemented. This methodology has been compared with other deep learning models verifying that the results of the designed neural network offer better performance in terms of quality measures.

Suggested Citation

  • Pérez-Sala, Luis & Curado, Manuel & Tortosa, Leandro & Vicent, Jose F., 2023. "Deep learning model of convolutional neural networks powered by a genetic algorithm for prevention of traffic accidents severity," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    • Handle: RePEc:eee:chsofr:v:169:y:2023:i:c:s0960077923001467
    DOI: 10.1016/j.chaos.2023.113245
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    References listed on IDEAS

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    1. Chen, Liang & Sun, Jingjie & Li, Kun & Li, Qiaoru, 2022. "Research on the effectiveness of monitoring mechanism for “yield to pedestrian” based on system dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    2. Li, Kun & Xu, Haocheng & Liu, Xiao, 2022. "Analysis and visualization of accidents severity based on LightGBM-TPE," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
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    1. Yang, Xiaoxia & Zhang, Guoqing & Shi, Baolong & Xie, Chuan-Zhi (Thomas) & Zhang, Botao, 2025. "Partition independent control and collaborative optimization of high-density crowd in subway stations," Chaos, Solitons & Fractals, Elsevier, vol. 200(P3).

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