research-article

A deep spatio-temporal attention-based neural network for passenger flow prediction

Authors:

Xingwu SunAuthors Info & Claims

MobiQuitous '19: Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

Pages 20 - 30

https://doi.org/10.1145/3360774.3360807

Published: 03 February 2020 Publication History

Abstract

Predicting the passenger flows in a city, especially in a metropolis, can guide traffic dispersion, and help assessing the risks of public safety and improving urban planning. However, it is challenging as passenger flows in a road network may vary with time and space, affected by weather conditions, urban activities, etc. In the paper, we propose a passenger flow prediction approach named Yildun, which constructs an encoder-decoder neural network and captures the spatial and temporal correlations inherent in passenger flows. More specifically, to predict the passenger flows at each and every station, a spatial attention mechanism is presented to adaptively extract inter-station correlations of flows by referring to the previous hidden state of the encoder at each time step. Meanwhile, a temporal attention mechanism is employed to capture time-dependent connections of flows by selecting relevant hidden states of the encoder across all time steps. Further, extra factors, such as POI (Point of Interest) data and day of the week, are fused in the decoder. With this spatio-temporal attention scheme, Yildun not only can make predictions effectively, but also is easily explainable. Extensive experiments are conducted on large-scale real-world data. The experimental results show that Yildun can predict passenger flows with small prediction errors and outperforms five baselines significantly.

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Wang WLiu BTian QXu XPeng YPeng S(2024)Predicting dust pollution from dry bulk ports in coastal cities: A hybrid approach based on data decomposition and deep learningEnvironmental Pollution10.1016/j.envpol.2024.124053350(124053)Online publication date: Jun-2024
https://doi.org/10.1016/j.envpol.2024.124053
Rocco di Torrepadula FNapolitano EDi Martino SMazzocca N(2024)Machine Learning for public transportation demand prediction: A Systematic Literature ReviewEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109166137(109166)Online publication date: Nov-2024
https://doi.org/10.1016/j.engappai.2024.109166
Sabzekar SBahmani RGhasemi MAmini Z(2024)Spatial Network-Wide Traffic Flow Imputation with Graph Neural NetworkInternational Journal of Intelligent Transportation Systems Research10.1007/s13177-024-00456-7Online publication date: 10-Dec-2024
https://doi.org/10.1007/s13177-024-00456-7
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Index Terms

A deep spatio-temporal attention-based neural network for passenger flow prediction
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Data streaming

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cover image ACM Other conferences

MobiQuitous '19: Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

November 2019

545 pages

ISBN:9781450372831

DOI:10.1145/3360774

General Chairs:
Vincent Poor
Princeton University
,
Zhu Han
University of Houston

Copyright © 2019 ACM.

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Association for Computing Machinery

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Publication History

Published: 03 February 2020

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Ministry of Transportation of China
National Natural Science Foundation of China

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MobiQuitous

MobiQuitous: Computing, Networking and Services

November 12 - 14, 2019

Texas, Houston, USA

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Overall Acceptance Rate 26 of 87 submissions, 30%

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Cited By

Wang WLiu BTian QXu XPeng YPeng S(2024)Predicting dust pollution from dry bulk ports in coastal cities: A hybrid approach based on data decomposition and deep learningEnvironmental Pollution10.1016/j.envpol.2024.124053350(124053)Online publication date: Jun-2024
https://doi.org/10.1016/j.envpol.2024.124053
Rocco di Torrepadula FNapolitano EDi Martino SMazzocca N(2024)Machine Learning for public transportation demand prediction: A Systematic Literature ReviewEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109166137(109166)Online publication date: Nov-2024
https://doi.org/10.1016/j.engappai.2024.109166
Sabzekar SBahmani RGhasemi MAmini Z(2024)Spatial Network-Wide Traffic Flow Imputation with Graph Neural NetworkInternational Journal of Intelligent Transportation Systems Research10.1007/s13177-024-00456-7Online publication date: 10-Dec-2024
https://doi.org/10.1007/s13177-024-00456-7
Goswami SSingh A(2024)A survey on overlapping community detection: label propagationMultimedia Tools and Applications10.1007/s11042-024-20485-4Online publication date: 16-Dec-2024
https://doi.org/10.1007/s11042-024-20485-4
Papastefanopoulos VLinardatos PPanagiotakopoulos TKotsiantis S(2023)Multivariate Time-Series Forecasting: A Review of Deep Learning Methods in Internet of Things Applications to Smart CitiesSmart Cities10.3390/smartcities60501146:5(2519-2552)Online publication date: 23-Sep-2023
https://doi.org/10.3390/smartcities6050114
Li BGuo TLi RWang YGandomi AChen F(2023)Self-Adaptive Predictive Passenger Flow Modeling for Large-Scale Railway SystemsIEEE Internet of Things Journal10.1109/JIOT.2023.327042710:16(14182-14196)Online publication date: 15-Aug-2023
https://doi.org/10.1109/JIOT.2023.3270427
Li BGuo TLi RWang YGandomi AChen F(2022)A Two-Stage Self-adaptive Model for Passenger Flow Prediction on Schedule-Based Railway SystemAdvances in Knowledge Discovery and Data Mining10.1007/978-3-031-05981-0_12(147-160)Online publication date: 10-May-2022
https://doi.org/10.1007/978-3-031-05981-0_12

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