A hybrid framework for synchronized passenger and train traffic simulation in an urban rail transit network

Hongxiang Zhang, Gongyuan Lu, Yuanzheng Lei, Guangyuan Zhang and Irene Niyitanga

International Journal of Rail Transportation, 2023, vol. 11, issue 6, 912-941

Abstract: Modelling passenger and train traffic is a significant approach to evaluate the performance of urban rail transit (URT) networks. However, the heavy computation pressure caused by high-efficiency requirements, massive passengers, and high network complexity makes it more challenging to integrate passenger and train traffic simulation into a unified model. We propose an efficient multi-agent model to simultaneously simulate passenger and train traffic in the URT network. The model framework comprises several agents, including passenger batch, train, line, and network. A passenger aggregation method is proposed to release the computation pressure. The model is tested in the URT network of Chongqing, China. The experiment results show the model can handle a 1.6 million passengers, 1900 trains simulation within 86 s, without losing any passengers’ specific travel spatial and temporal trajectory. Three experiments are conducted for further validation, including analysing the transportation performance under different passenger route assignments, train headways, and AFC data, respectively.

Date: 2023
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DOI: 10.1080/23248378.2022.2109522

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