Collaborative passenger flow control for an urban rail transit network

Zhao, Qian; Tang, Jinjin; Zhang, Xin

doi:10.1111/mice.13046

Cited by 2 publications

(2 citation statements)

References 71 publications

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“…(Cong et al 2022) proposed a multi-agent-based simulation approach to estimating the impacts of unplanned rail line segment disruption on passengers. (Zhao et al 2023) introduced a forward-backward algorithm to solve the proposed collaborative passenger flow control model, aiming to minimize the difference between the expected number of passengers on bottleneck links and the number of passengers with control. These simulationbased methods provide valuable insights into the dynamic and stochastic nature of passenger flow in metro networks, especially in developing effective control strategies to enhance system performance and passenger experience.…”

Section: Passenger Flow Controlmentioning

confidence: 99%

“…Recently, (Nguyen et al 2021) proposed a prioritized objective actor-critic method that combines the Multi-Critic Single Policy (MCSP) and Single Critic Multi-Policy (SCMP) to optimize the mean of total rewards. (Zhu et al 2023) Min passenger waiting time Analytical approximation-based (Zhao et al 2023) Min controlled source points and passengers, Min the difference between the expected number of passengers and those with control Optimizer tools (Cong et al 2022) Min passenger waiting time Simulationbased - (Lei et al 2022) Min the number of bottlenecks - (Jiang et al 2018) Min the safety risks (penalty value) Machine learning-based Q-learning (Jiang et al 2019) Min the safety risks (penalty value) Q-learning (Abdulhai and Kattan 2003) provided a comprehensive overview of RL and its successful applications in the field of traffic control and transportation engineering. The study synthesized the concept of RL and its potential benefits for the transportation industry.…”

Section: Reinforcement Learning In Traffic and Transit Operationsmentioning

confidence: 99%

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A Reinforcement Method for Passenger Flow Control Based on Asynchronous Advantage Actor-Critic Neural Network

Wang

Jin

Luo

et al. 2023

Preprint

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Effective passenger flow management is critical for improving service quality and alleviating congestion in metro networks. However, the dynamic nature of travel demand and the complex structure of metro networks present significant challenges in building and solving control models. Additionally, the high computational costs of existing methods limit their practical applications. To address these challenges, this study proposes a new reinforcement learning (RL) based method for passenger flow control. The method has three components: the network state characterization, the control model, and the reinforcement learning model. Then, the study outlines the “action”, “state”, and “reward” concepts in RL based on the definition of decision variables, constraints, and objective functions in the constructed passenger flow control programming model. An iterative interaction mechanism is introduced to synchronize the control schemes generated by the reinforcement learning unit and the network states. Furthermore, effectively utilizing computational resources, the Asynchronous Advantage Actor-Critic Neural Network (A3C-NN) is trained to optimize the complex programming model. Finally, the proposed approach is validated through a case study using data from Chengdu Urban Rail Transit (URT), demonstrating its effectiveness in achieving various objectives, such as minimizing passenger waiting time, maximizing passenger turnover, and maximizing passenger numbers.

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Section: Passenger Flow Controlmentioning

confidence: 99%