New stations of urban rail based on TOD model classification inbound and outbound passenger traffic forecasts: the case of Chengdu

Qiang Wang; Yugang Liu; Shuai Zheng; Bin Chen; Ya hui Li

doi:10.1117/12.2668791

16 February 2023 New stations of urban rail based on TOD model classification inbound and outbound passenger traffic forecasts: the case of Chengdu

Qiang Wang, Yugang Liu, Shuai Zheng, Bin Chen, Ya hui Li

Author Affiliations +

Proceedings Volume 12591, Sixth International Conference on Traffic Engineering and Transportation System (ICTETS 2022); 125910O (2023) https://doi.org/10.1117/12.2668791
Event: 6th International Conference on Traffic Engineering and Transportation System (ICTETS 2022), 2022, Guangzhou, China

Abstract

In order to grasp the pattern of passenger flow changes at the initial opening of new urban rail transit TOD stations and to address the problem of irregular fluctuations in passenger flow at new stations and the absence of historical data, this paper proposes a method for predicting passenger flow in and out of new urban rail transit stations based on the TOD mode division. Firstly, through the analysis of the classification method of traditional TOD stations and the classification index of TOD stations, the TOD characteristic similarity urban rail stations are classified based on the hierarchical clustering algorithm. Secondly, according to the station classification results, a method is proposed to construct a historical database of passenger flow in and out of new stations. Then, by optimizing the parameters of the differential integrated moving average autoregressive model, a short-time prediction method for passenger flow in and out of new stations of urban rail TOD is proposed. Finally, the model is tested with the actual passenger flow data of the urban rail line network. The results show that the prediction errors of incoming and outgoing passenger flows are 2.59% and 7.67%, respectively, indicating that the prediction method is effective and can be applied to the pre-construction planning of new TOD stations. The prediction results can provide a reference for the urban rail traffic organization.

Citation Download Citation

Qiang Wang, Yugang Liu, Shuai Zheng, Bin Chen, and Ya hui Li "New stations of urban rail based on TOD model classification inbound and outbound passenger traffic forecasts: the case of Chengdu", Proc. SPIE 12591, Sixth International Conference on Traffic Engineering and Transportation System (ICTETS 2022), 125910O (16 February 2023); https://doi.org/10.1117/12.2668791

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KEYWORDS

Databases

Data modeling

Autoregressive models

Neural networks

Transportation

Autocorrelation

Design and modelling

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