Online Car-Hailing Origin-Destination Forecast Based on a Temporal Graph Convolutional Network-Reference-Cited by-同舟云学术

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Online Car-Hailing Origin-Destination Forecast Based on a Temporal Graph Convolutional Network

Published:2023-07 Issue:4 Volume:15 Page:121-136
ISSN:1939-1390
Container-title:IEEE Intelligent Transportation Systems Magazine
language:
Short-container-title:IEEE Intell. Transport. Syst. Mag.

Author:

Shuai Chunyan¹^ORCID,Zhang Xiaoqi¹^ORCID,Wang Yuxiang²,He Mingwei¹^ORCID,Yang Fang¹,Xu Geng³

Affiliation:

1. Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming, China

2. Information Center, Henan Cancer Hospital, Zhengzhou, China

3. Information Research Centre, Kunming Urban Planning and Design Institute, Kunming, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/5117645/10174808/10056421.pdf?arnumber=10056421

Reference35 articles.

1. DeepSD: Supply-Demand Prediction for Online Car-Hailing Services Using Deep Neural Networks

2. T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction

3. Dynamic Origin-Destination Prediction in Urban Rail Systems: A Multi-Resolution Spatio-Temporal Deep Learning Approach

4. Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems

5. A Graph Convolutional Stacked Bidirectional Unidirectional-LSTM Neural Network for Metro Ridership Prediction

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