Multi‐graph convolutional network for short‐term passenger flow forecasting in urban rail transit-Reference-Cited by-同舟云学术

Multi‐graph convolutional network for short‐term passenger flow forecasting in urban rail transit

Published:2020-10 Issue:10 Volume:14 Page:1210-1217
ISSN:1751-956X
Container-title:IET Intelligent Transport Systems
language:en
Short-container-title:IET intell. transp. syst

Author:

Zhang Jinlei¹,Chen Feng¹²³,Guo Yinan⁴,Li Xiaohong¹

Affiliation:

1. School of Civil Engineering, Beijing Jiaotong UniversityNo. 3 Shangyuancun, Haidian DistrictBeijing100044People's Republic of China

2. School of Highway, Chang'an UniversityMiddle‐Section of Nan'er Huan RoadXi'an710064People's Republic of China

3. Beijing Engineering and Technology Research Centre of Rail Transit Line Safety and Disaster PreventionNo.3 Shangyuancun, Haidian DistrictBeijing100044People's Republic of China

4. Information School, University of WashingtonSeattleWAUSA

Publisher

Institution of Engineering and Technology (IET)

Subject

Law,Mechanical Engineering,General Environmental Science,Transportation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-its.2019.0873

Reference44 articles.

1. Traffic Flow Forecasting: Comparison of Modeling Approaches

2. Adaptive Seasonal Time Series Models for Forecasting Short-Term Traffic Flow

3. Traffic forecasting using least squares support vector machines

4. Hybrid GA Based Online Support Vector Machine Model for Short-Term Traffic Flow Forecasting

5. Short Term Traffic Flow Prediction for a Non Urban Highway Using Artificial Neural Network

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