Multi-time granularity subway line network short-time OD passenger flow forecasting based on LightGBM model

Heng Zhang; Weizhou Xiao; Mingjiao Zhang

doi:10.1117/12.2672715

16 March 2023 Multi-time granularity subway line network short-time OD passenger flow forecasting based on LightGBM model

Heng Zhang, Weizhou Xiao, Mingjiao Zhang

Author Affiliations +

Proceedings Volume 12593, Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022); 125931L (2023) https://doi.org/10.1117/12.2672715
Event: 2nd Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022), 2022, Guangzhou, China

Abstract

In order to accurately obtain the short-time OD passenger flow distribution of the subway line network, so as to efficiently coordinate the transportation capacity and passenger demand, a multi-time granularity subway line network short-time OD passenger flow prediction model based on LightGBM was constructed by combining the idea of ensemble learning. The model uses the subway automatic ticket sales and inspection data to analyze the temporal and spatial distribution characteristics of OD passenger flow on the line network, introduces a variety of temporal and spatial influencing factors to train and predict the data of the whole network, and studies the relationship between the prediction accuracy of the subway line network OD passenger flow and the time granularity. relationship between. Taking the Suzhou subway as an example, the results show that: compared with other models, the model can not only effectively reduce the prediction error, but also can effectively fit the peak passenger flow, and improve the accuracy of short-time OD passenger flow prediction of the subway network.

Citation Download Citation

Heng Zhang, Weizhou Xiao, and Mingjiao Zhang "Multi-time granularity subway line network short-time OD passenger flow forecasting based on LightGBM model", Proc. SPIE 12593, Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022), 125931L (16 March 2023); https://doi.org/10.1117/12.2672715

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