Lane-Level Short-Term Freeway Traffic Volume Prediction Based on Graph Convolutional Recurrent Network-Reference-Cited by-同舟云学术

Lane-Level Short-Term Freeway Traffic Volume Prediction Based on Graph Convolutional Recurrent Network

Published:2023-10 Issue:10 Volume:149 Page:
ISSN:2473-2907
Container-title:Journal of Transportation Engineering, Part A: Systems
language:en
Short-container-title:J. Transp. Eng., Part A: Systems

Author:

Liu Lu¹,Cui Zhiyong²^ORCID,Ke Ruimin³,Wang Yinhai⁴^ORCID

Affiliation:

1. Research Assistant, Automotive Transportation Technology Research Center, Research Institute of Highway Ministry of Transport, Beijing 100088, China.

2. Professor, School of Transportation Science and Engineering, Beihang Univ., Beijing 100191, China (corresponding author). ORCID: .

3. Assistant Professor, Dept. of Civil Engineering, Univ. of Texas at El Paso, El Paso, TX 79968.

4. Professor, Dept. of Civil and Environmental Engineering, Univ. of Washington, Seattle, WA 98105. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Transportation,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/JTEPBS.TEENG-7868

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4. Framewise phoneme classification with bidirectional LSTM and other neural network architectures

5. Short-term prediction of lane-level traffic speeds: A fusion deep learning model;Gu Y.;Transp. Res. Part C Emerging Technol.,2019