Strategy decision on cycling race based on genetic algorithm

Xiaokuan Chang; Hang Ning; Hongpeng Liu; Kehan Chen

doi:10.1117/12.2686045

28 July 2023 Strategy decision on cycling race based on genetic algorithm

Xiaokuan Chang, Hang Ning, Hongpeng Liu, Kehan Chen

Proceedings Volume 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023); 1275648 (2023) https://doi.org/10.1117/12.2686045
Event: 2023 3rd International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2023), 2023, Tangshan, China

Abstract

This paper focuses on the individual time trial and has a study of the strategy selection for the male Sprinter and Time Specialist. Firstly, the general characteristics of the sprinters and time specialists are given according to 4 abilities. And the terrains of the track are defined by the curvature and slope. Also, the strategies of the riders are classified into dashing, speeding up, keeping a constant velocity, and slowing down. Then, the power-consumed model is established. We choose the course of 2021 Olympic Time Trial course in Tokyo as an example and get the data of the track. To get the velocity of the riders at every step, Newton Iterative Method is used. And Genetic Algorithm is applied to get the optimal strategy selection for the riders to take the shortest time. To validate the model, the time the champion, who is a sprinter, takes is compared to this example, which proves the reliability of the model. Finally, the results show that the sprinters should apply less power on the downhill road to storage more power for the uphill, and time specialists should spend less power on the uphill and recover the energy in time to prepare for next explosiveness. And they both need to dash at the end

Citation Download Citation

Xiaokuan Chang, Hang Ning, Hongpeng Liu, and Kehan Chen "Strategy decision on cycling race based on genetic algorithm", Proc. SPIE 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023), 1275648 (28 July 2023); https://doi.org/10.1117/12.2686045

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