Version 1
: Received: 25 May 2023 / Approved: 26 May 2023 / Online: 26 May 2023 (09:33:38 CEST)
How to cite:
Yang, W.; Wu, H. Optimization of the Subway Platform’s Passenger Flow Guidance Method Using Anylogic. Preprints2023, 2023051898. https://doi.org/10.20944/preprints202305.1898.v1
Yang, W.; Wu, H. Optimization of the Subway Platform’s Passenger Flow Guidance Method Using Anylogic. Preprints 2023, 2023051898. https://doi.org/10.20944/preprints202305.1898.v1
Yang, W.; Wu, H. Optimization of the Subway Platform’s Passenger Flow Guidance Method Using Anylogic. Preprints2023, 2023051898. https://doi.org/10.20944/preprints202305.1898.v1
APA Style
Yang, W., & Wu, H. (2023). Optimization of the Subway Platform’s Passenger Flow Guidance Method Using Anylogic. Preprints. https://doi.org/10.20944/preprints202305.1898.v1
Chicago/Turabian Style
Yang, W. and Huirong Wu. 2023 "Optimization of the Subway Platform’s Passenger Flow Guidance Method Using Anylogic" Preprints. https://doi.org/10.20944/preprints202305.1898.v1
Abstract
This study aims to optimize the subway platform passenger flow guidance method using Anylogic simulation software. The objective of the study is to minimize overcrowding within subway carriages, enhance transportation capacity, and augment service levels by guiding passengers on the platform to board trains in a distributed manner. The study achieves a balanced flow of passengers in each carriage by taking into consideration the number of passengers in each carriage, the number of passengers at the station, and the expected number of passengers disembarking from the trains. The standard deviation of balance is used to optimize the subway platform passenger flow guidance method. We simulated Guangzhou Metro Zhujiang New Town Line 5 platform to compare different guidance methods and evaluate their effects on the uneven distribution of passengers with passenger flows ranging from 120K to 600K people per day. Our results show that guidance method 2, relative to manual guidance, improves by 38.5% under a daily passenger flow of 240K people on Zhujiang New Town Line 5. Guidance method 3 improves by 25.7% relative to guidance method 2 and by 54.4% relative to manual guidance.
Keywords
Urban Rail Transit; Passenger Flow Guidance; Anylogic Simulation; Standard Deviation of Balance
Subject
Engineering, Transportation Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.