Welcome to Francis Academic Press

Frontiers in Sport Research, 2022, 4(4); doi: 10.25236/FSR.2022.040407.

Best Pacing Strategy: Time Trial Optimization with Physiological and Power Simulation


Yuhao Wang, Li Wang, Junyu Ge

Corresponding Author:
Yuhao Wang

School of Electronic and Optical Engineering, NanJing University of Science and Technology, Nanjing, Jiangsu, 210000, China


In this paper, we establish a dynamic programming model of the motion process and a dynamic simulation model of the riding process, and use the genetic algorithm to simulate and optimize the speed and force distribution of the rider during the riding process. Specifically, we established a dynamic power curve model determined by a physiological mechanism (lactate heart rate), and derived the dynamic power curves of different genders, different levels, and different types of riders. In addition, we established a timed trial simulation and pacing strategy optimization model based on the micro-element method and random velocity generation, and used genetic algorithm to solve it. Further, we build a tempo strategy optimization model for team games. The optimal solution is obtained by using the improved objective function of team competition in genetic algorithm. Finally, a sensitivity analysis was performed for the considered models.


Self-feedback system, Micro-element, Genetic algorithm, Dynamic power curve

Cite This Paper

Yuhao Wang, Li Wang, Junyu Ge. Best Pacing Strategy: Time Trial Optimization with Physiological and Power Simulation. Frontiers in Sport Research (2022) Vol. 4, Issue 4: 35-43. https://doi.org/10.25236/FSR.2022.040407.


[1] Atkinson G, Peacock O, Gibson A, et al. Distribution of power output during cycling [J]. Sports Medicine, 2007, 37(8): 647-667.

[2] Atkinson G, Peacock O, Law M. Acceptability of power variation during a simulated hilly time trial. Int J Sports Med 2007; 28: 157–63.

[3] Karvonen, J., Vuorimaa, T. Heart rate and exercise intensity during sports activities. Sports Medicine 5, 303–311 (1988).

[4] Song Haibin, Xu Bo Comparison between “anaerobic threshold” and “individual lactate threshold” theory in aerobic training [J] Journal of Leshan Normal University, 2005, 20 (5): 2.

[5] Gastin P B, Cassy C, Dan D. Validity of the Acti Graph GT3X+ and Body Media Sense Wear Armband to estimate energy expenditure during physical activity and sport[J]. Journal of science and medicine in sport, 2019, 21(3):291-295.

[6] Farhadi S, Sabet M S, Malboobi M A. The Critical Role of AtPAP17 and AtPAP26 Genes in Arabidopsis Phosphate Compensation Network[J]. Frontiers in Plant Science, 2020, 11.