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Neural Computing and Applications

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02.01.2021 | Original Article

A queuing network simulation optimization method for coordination control of passenger flow in urban rail transit stations

verfasst von: Jun Liu, Lu Hu, Xinpei Xu, Jiayuan Wu

Erschienen in: Neural Computing and Applications | Ausgabe 17/2021

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Abstract

The imbalances between supply and demand in an urban rail transit system have received extensive attention. Addressing these imbalances by controlling the movements of passengers in the system is a real challenge. To complete the peak passenger flow control scheme, a novel discrete event simulation (DES) optimization model based on queuing theory is proposed to minimize the urban rail transit company losses and the passenger time delays considering effects of congestion propagation among facilities at busy stations. The proposed approach can generate a control scheme that consists of the controlling number of entering passengers and the controlling parameters of facilities inside the station. The first stage of the method models the subway network using queuing theory and then builds a DES model that is based on an urban rail transit queuing network. In the second stage, a service-security-economic-oriented optimization model is established and combined with a queuing network simulation model to implement the simulation optimization. We present three numerical experiments to compare the optimization results in different passenger flow scenarios. The results demonstrate that the DES optimization method in this paper can provide a reasonable and reliable control scheme in daily operations.

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Titel: A queuing network simulation optimization method for coordination control of passenger flow in urban rail transit stations
verfasst von: Jun Liu
Lu Hu
Xinpei Xu
Jiayuan Wu
Publikationsdatum: 02.01.2021
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 17/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05580-5

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