Jointly optimized demand-oriented train timetable and passenger flow control strategy for a congested subway line under a short-turning operation pattern

Xue, Hongjiao; Jia, Limin; Li, Jian; Guo, Jianyuan

doi:10.1016/j.physa.2022.126957

Cited by 9 publications

(2 citation statements)

References 35 publications

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“…Achieving integrated optimization of the train flow and passenger flow is the key to improving the overall efficiency of the metro system. Some studies propose train scheduling with collaborative passenger flow control on oversaturated metro lines, limiting the number of passengers from entering platforms to prevent congestion [10,11,12,13]. Shang [14] developed a train operation and passenger distribution spacetime network to optimize train skip-stopping pattern, schedules, and passenger flow control strategies.…”

Section: Introductionmentioning

confidence: 99%

A Collaborative Optimization Method for Train Scheduling and Passenger Flow Assignment Based on Multi-Agent Reinforcement Learning

Ning,

Dong,

Sun

et al. 2024

Advances in Transdisciplinary Engineering

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This paper presents an online optimization method for metro network train scheduling and passenger flow assignment based on multi-agent reinforcement learning, aiming at minimizing traction energy consumption and average passenger waiting time. The problem is modeled as a multi-agent Markov decision process using a multi-agent actor-critic framework for network train scheduling and a deep deterministic policy gradient framework for passenger flow assignment. All agents interact with the same metro simulation environment, which generates train timetables and passenger flow assignments that meet complex constraints. Results of the case study on anonymized data of Chongqing Metro show that the proposed method outperforms baseline scenarios and is able to adjust train schedules and passenger flow assignments in real-time when passenger flow distribution fluctuates, demonstrating its effectiveness and robustness.

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Section: Introductionmentioning

confidence: 99%

A Collaborative Optimization Method for Train Scheduling and Passenger Flow Assignment Based on Multi-Agent Reinforcement Learning

Ning,

Dong,

Sun

et al. 2024

Advances in Transdisciplinary Engineering

View full text Add to dashboard Cite

show abstract

“…Timed transfer focuses on scheduling strategy to maximize the number of simultaneous arrivals at transfer stations [5][6][7][8][9][10]. Taking time consumption into account, the objective of transfer optimization is to reduce the transfer time by adjustments of the departure time, running time between stations and dwell time [11][12][13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%