@article{ref1,
title="Forecasting subway passenger flow for station-level service supply",
journal="Big data",
year="2022",
author="Tu, Qun and Zhang, Qianqian and Zhang, Zhenji and Gong, Daqing and Jin, Chenxi",
volume="ePub",
number="ePub",
pages="ePub-ePub",
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.<p /> <p>Language: en</p>",
language="en",
issn="2167-6461",
doi="10.1089/big.2021.0318",
url="http://dx.doi.org/10.1089/big.2021.0318"
}