Spatiotemporal Attention Fusion Network for Short-Term Passenger Flow Prediction on New Year’s Day Holiday in Urban Rail Transit System-Reference-Cited by-同舟云学术

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Spatiotemporal Attention Fusion Network for Short-Term Passenger Flow Prediction on New Year’s Day Holiday in Urban Rail Transit System

Published:2023-09 Issue:5 Volume:15 Page:59-77
ISSN:1939-1390
Container-title:IEEE Intelligent Transportation Systems Magazine
language:
Short-container-title:IEEE Intell. Transport. Syst. Mag.

Author:

Zhang Shuxin¹^ORCID,Zhang Jinlei¹^ORCID,Yang Lixing¹^ORCID,Yin Jiateng¹,Gao Ziyou¹

Affiliation:

1. School of Systems Science, Beijing Jiaotong University, Beijing, China

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Double First-Class Major Research Programs, Educational Department of Gansu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/5117645/10242178/10111528.pdf?arnumber=10111528

Reference46 articles.

1. Traffic flow prediction using Adaboost algorithm with random forest as a weak learner;leshem;Proc World Acad Sci Eng Technol,2007

2. Forecasting road traffic speeds by considering area-wide spatio-temporal dependencies based on a graph convolutional neural network (GCN)

3. Short-term traffic flow prediction using seasonal ARIMA model with limited input data

4. Urban rail transit passenger flow forecast based on LSTM with enhanced long‐term features

5. Research and Comparison of ARIMA and Grey Prediction Models for Subway Traffic Forecasting

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4. PLU-MCN: Perturbation learning enhanced U-shaped multi-graph convolutional network for traffic flow prediction;Information Fusion;2024-04

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