Citation

Tu Q, Zhang Q, Zhang Z, Gong D, Jin C. Big Data 2022; ePub(ePub): ePub.

Copyright

(Copyright © 2022, Mary Ann Liebert Publishers)

DOI

10.1089/big.2021.0318

PMID

35749714

Abstract

Demand forecasting is one of the managers' concerns in service supply chain management. With accurate passenger flow forecasting, the station-level service suppliers can make better service plans accordingly. However, the existing forecasting model cannot identify the different future passenger flow at different types of stations. As a result, the service suppliers cannot make service plans according to the demands of different stations. In this article, we propose a deep learning architecture called DeepSPF (Deep Learning for Subway Passenger Forecasting) to predict subway passenger flow considering the different functional types of stations. We also propose the sliding long short-term memory (LSTM) neural networks as an important component of our model, combining LSTM and one-dimensional convolution. In the experiments of the Beijing subway, DeepSPF outperforms the baseline models in three-time granularities (10, 15, and 30 minutes). Moreover, a comparison between variants of DeepSPF indicates that, with the information of stations' functional types, DeepSPF has strong robustness when an abnormal situation happens.

Language: en

Keywords

deep learning; demand forecasting; intelligent transportation systems