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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A Deep Reinforcement Learning Approach for the Energy-Aimed Train Timetable Rescheduling Problem Under Disturbances
Jinlin Liao, Guang Yang, Shiwen Zhang, et al.
IEEE Transactions on Transportation Electrification (2021) Vol. 7, Iss. 4, pp. 3096-3109
Closed Access | Times Cited: 36

Showing 1-25 of 36 citing articles:

Cooperative train control during the power supply shortage in metro system: A multi-agent reinforcement learning approach
Xuekai Wang, Andrea D’Ariano, Shuai Su, et al.
Transportation Research Part B Methodological (2023) Vol. 170, pp. 244-278
Closed Access | Times Cited: 27
How airports enhance the environmental sustainability of operations: A critical review from the perspective of Operations Research
S.H. Chen, Lingxiao Wu, Kam K.H. Ng, et al.
Transportation Research Part E Logistics and Transportation Review (2024) Vol. 183, pp. 103440-103440
Closed Access | Times Cited: 8
Applications of Genetic Algorithm and Its Variants in Rail Vehicle Systems: A Bibliometric Analysis and Comprehensive Review
Hamed Jafari Kaleybar, M.M. Davoodi, Morris Brenna, et al.
IEEE Access (2023) Vol. 11, pp. 68972-68993
Open Access | Times Cited: 20
Traction power supply system of China high-speed railway under low-carbon target: Form evolution and operation control
Xin Li, Chengkun Zhu, Yingzhi Liu
Electric Power Systems Research (2023) Vol. 223, pp. 109682-109682
Closed Access | Times Cited: 17
Distributed Multiagent Deep Reinforcement Learning for Multiline Dynamic Bus Timetable Optimization
Haoyang Yan, Zhiyong Cui, Xinqiang Chen, et al.
IEEE Transactions on Industrial Informatics (2022) Vol. 19, Iss. 1, pp. 469-479
Closed Access | Times Cited: 24
Integrated train timetabling and rolling stock rescheduling for a disturbed metro system: A hybrid deep reinforcement learning and adaptive large neighborhood search approach
Boyi Su, Andrea D’Ariano, Shuai Su, et al.
Computers & Industrial Engineering (2023) Vol. 186, pp. 109742-109742
Closed Access | Times Cited: 15
Single-track railway scheduling with a novel gridworld model and scalable deep reinforcement learning
Feiyu Yang, Yuhua Yang, Shaoquan Ni, et al.
Transportation Research Part C Emerging Technologies (2023) Vol. 154, pp. 104237-104237
Closed Access | Times Cited: 12
Solving the train dispatching problem via deep reinforcement learning
Valerio Agasucci, Giorgio Grani, Leonardo Lamorgese
Journal of Rail Transport Planning & Management (2023) Vol. 26, pp. 100394-100394
Open Access | Times Cited: 10
Contrastive Learning-Based Bayes-Adaptive Meta-Reinforcement Learning for Active Pantograph Control in High-Speed Railways
Hui Wang, Zhiwei Han, Xufan Wang, et al.
IEEE Transactions on Transportation Electrification (2023) Vol. 10, Iss. 1, pp. 2045-2056
Closed Access | Times Cited: 9
A hybrid machine learning approach for train trajectory reconstruction under interruptions considering passenger demand
Zishuai Pang, Liwen Wang, Li Li
International Journal of Rail Transportation (2024), pp. 1-29
Closed Access | Times Cited: 3
Robust dynamic train regulation integrated with stop-skipping strategy in urban rail networks: An outer approximation based solution method
Zebin Chen, Andrea D’Ariano, Shukai Li, et al.
Omega (2024) Vol. 128, pp. 103135-103135
Closed Access | Times Cited: 3
Robust Optimization of Energy-Saving Train Trajectories Under Passenger Load Uncertainty Based on p-NSGA-II
Xing Chen, Kang Li, Li Zhang, et al.
IEEE Transactions on Transportation Electrification (2022) Vol. 9, Iss. 1, pp. 1826-1844
Open Access | Times Cited: 15
Real-Time Adjustment Method for Metro Systems with Train Delays Based on Improved Q-Learning
Yushen Hu, Wei Li, Qin Luo
Applied Sciences (2024) Vol. 14, Iss. 4, pp. 1552-1552
Open Access | Times Cited: 2
Adaptive rescheduling of rail transit services with short-turnings under disruptions via a multi-agent deep reinforcement learning approach
Cheng-shuo Ying, Andy H.F. Chow, Yimo Yan, et al.
Transportation Research Part B Methodological (2024) Vol. 188, pp. 103067-103067
Closed Access | Times Cited: 2
Model-based Data Efficient Reinforcement Learning for Active Pantograph Control in High-Speed Railways
Hui Wang, Zhigang Liu, Xufan Wang, et al.
IEEE Transactions on Transportation Electrification (2023) Vol. 10, Iss. 2, pp. 2701-2712
Closed Access | Times Cited: 6
Energy-Saving Train Operation Synergy Based on Multi-Agent Deep Reinforcement Learning on Spark Cloud
Mengying Shang, Yonghua Zhou, Yiduo Mei, et al.
IEEE Transactions on Vehicular Technology (2022) Vol. 72, Iss. 1, pp. 214-226
Closed Access | Times Cited: 8
A Critical Review of Subway Train Timetabling and Rescheduling Problems
Liujiang Kang, Nsabimana Buhigiro, Huijun Sun, et al.
IEEE Transactions on Intelligent Transportation Systems (2024) Vol. 25, Iss. 10, pp. 12930-12942
Closed Access | Times Cited: 1
Reduction of power transmission losses in high-speed rail systems through efficient scheduling
Jesús Riego Martínez, Marcelo Perez‐Alonso, Óscar Duque-Pérez
International Journal of Electrical Power & Energy Systems (2024) Vol. 160, pp. 110123-110123
Open Access | Times Cited: 1
Neural networked-assisted method for the nurse rostering problem
Ziyi Chen, Yajie Dou, Patrick De Causmaecker
Computers & Industrial Engineering (2022) Vol. 171, pp. 108430-108430
Closed Access | Times Cited: 6
An energy‐efficient train control approach with dynamic efficiency of the traction system
Chengcheng Fu, Pengfei Sun, Jiahui Zhang, et al.
IET Intelligent Transport Systems (2023) Vol. 17, Iss. 6, pp. 1182-1199
Open Access | Times Cited: 2
A bi-objective optimization model of metro trains considering energy conservation and passenger waiting time
Zheng Sun, Deqiang He, Yan He, et al.
Journal of Cleaner Production (2023) Vol. 437, pp. 140427-140427
Closed Access | Times Cited: 2
Optimization of Train Operation Control Based on Soft Actor-Critic Deep Reinforcement Learning Algorithm
Zhuyuan Lan, Huiqin Pei
Lecture notes in electrical engineering (2024), pp. 88-96
Closed Access
A Collaborative Optimization Method for Train Scheduling and Passenger Flow Assignment Based on Multi-Agent Reinforcement Learning
Xinyi Ning, Wei Dong, Xinya Sun, et al.
Advances in transdisciplinary engineering (2024)
Open Access
Sustainable Multi-Modal Transit Timetabling with Deep Learning
Ke Zhang, Yucheng Shi, Siobhán Clarke
(2024)
Closed Access
Economic Evaluation of Using Ultracapacitors in Electric Vehicles
Hossein Rezaei, Seyed Ehsan Abdollahi, Seyedreza Abdollahi, et al.
Energy Technology (2024)
Closed Access
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