Optimization of Train Operation Control Based on Soft Actor-Critic Deep Reinforcement Learning Algorithm
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-9311-6_10
Reference20 articles.
1. Lin, B., Zhao, Y., Lin, R., et al.: Integrating traffic routing optimization and train formation plan using simulated annealing algorithm. Appl. Math. Model. 93, 811–830 (2021)
2. Zhao, H., Dai, X.W.: Event-triggered adaptive control for multiple high-speed trains with deception attacks in bottleneck sections. Inf. Sci. 547, 470–481 (2021)
3. Wang, X., Tang, T., Su, S., et al.: An integrated energy-efficient train operation approach based on the space-time-speed network methodology. Transp. Res. Part E: Logist. Transp. Rev. 150, 102323 (2021)
4. Huang, K., Wu, J., Yang, X., et al.: Discrete train speed profile optimization for urban rail transit: a data-driven model and integrated algorithms based on machine learning. J. Adv. Transp. 2019, 7258986 (2019)
5. Zhao, L., Peng, J., Wang, J., et al.: Optimization control of energy-efficient driving for trains in urban rail transit based on GA-PSO algorithm. In: 4th International Conference on Electrical and Information Technologies for Rail Transportation, Novel Traction Drive Technologies of Rail Transportation, pp. 777–785. Springer, Singapore (2020)
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