Reinforcement learning approach for coordinated passenger inflow control of urban rail transit in peak hours-Reference-Cited by-同舟云学术

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Reinforcement learning approach for coordinated passenger inflow control of urban rail transit in peak hours

Published:2018-03 Issue: Volume:88 Page:1-16
ISSN:0968-090X
Container-title:Transportation Research Part C: Emerging Technologies
language:en
Short-container-title:Transportation Research Part C: Emerging Technologies

Author:

Jiang Zhibin^ORCID,Fan Wei^ORCID,Liu Wei,Zhu Bingqin,Gu Jinjing

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Elsevier BV

Subject

Computer Science Applications,Transportation,Automotive Engineering,Civil and Structural Engineering

Reference28 articles.

1. Reinforcement learning-based multi-agent system for network traffic signal control;Arel;IET Intel. Transport Syst.,2010

2. Macroscopic pedestrian flow simulation for designing crowd control measures in public transport after special events;Bauer,2007

3. A multiagent-based model for pedestrian simulation in subway stations;Chen;Simul. Model. Pract. Theory,2017

4. Methodology for determining dwell times consistent with passenger flows in the case of metro services;D’Acierno;Urban Rail Transit,2017

5. How much can holding and/or limiting boarding improve transit performance?;Delgado;Transp. Res. Part B: Methodol.,2012

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