ST-InNet: Deep Spatio-Temporal Inception Networks for Traffic Flow Prediction in Smart Cities-Reference-Cited by-同舟云学术

ST-InNet: Deep Spatio-Temporal Inception Networks for Traffic Flow Prediction in Smart Cities

Published:2022-10 Issue:10 Volume:23 Page:19782-19794
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Dai Fei¹^ORCID,Huang Penggui¹,Mo Qi²^ORCID,Xu Xiaolong³^ORCID,Bilal Muhammad⁴^ORCID,Song Houbing⁵^ORCID

Affiliation:

1. School of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, China

2. School of Software, Yunnan University, Kunming, China

3. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China

4. Division of Computer and Electronics Systems Engineering, Hankuk University of Foreign Studies, Yongin-si, South Korea

5. Department of Electrical Engineering and Computer Science, Security and Optimization for Networked Globe Laboratory (SONG Lab), Embry-Riddle Aeronautical University, Daytona Beach, FL, USA

Funder

Project of Key Science Foundation of Yunnan Province

Dou Wanchun Expert Workstation of Yunnan Province

Project of National Natural Science Foundation of China

Major Project of Science and Technology of Yunnan Province

Project of Science and Technology of Yunnan Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/9916643/09793390.pdf?arnumber=9793390

Reference41 articles.

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

2. Dynamic spatial-temporal representation learning for traffic flow prediction;liu;IEEE Trans Intell Transp Syst,2020

3. Improvement of Search Strategy With K-Nearest Neighbors Approach for Traffic State Prediction

4. Short-term traffic predictions on large urban traffic networks: Applications of network-based machine learning models and dynamic traffic assignment models

5. A Bayesian Network Approach to Traffic Flow Forecasting

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