Hybrid Recurrent Neural Network Modeling for Traffic Delay Prediction at Signalized Intersections Along an Urban Arterial-Reference-Cited by-同舟云学术

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Hybrid Recurrent Neural Network Modeling for Traffic Delay Prediction at Signalized Intersections Along an Urban Arterial

Published:2023-01 Issue:1 Volume:24 Page:1384-1394
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Subramaniyan Arun Bala¹,Wang Chieh²^ORCID,Shao Yunli²^ORCID,Li Wan²,Wang Hong²,Zhang Guohui¹^ORCID,Ma Tianwei³^ORCID

Affiliation:

1. Department of Civil and Environmental Engineering, University of Hawaii at Māanoa, Honolulu, HI, USA

2. Oak Ridge National Laboratory, Buildings and Transportation Science Division, Oak Ridge, TN, USA

3. College of Engineering, Texas A&M University-Corpus Christi, Corpus Christi, TX, USA

Funder

U

National Science Foundation Major Research Instrumentation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

https://ieeexplore.ieee.org/ielam/6979/10026638/9875065-aam.pdf

Reference27 articles.

1. Recent Advances in Reinforcement Learning for Traffic Signal Control

2. Traffic Signal Control With Adaptive Online-Learning Scheme Using Multiple-Model Neural Networks

3. Toward A Thousand Lights: Decentralized Deep Reinforcement Learning for Large-Scale Traffic Signal Control

4. Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning

5. Modeling and Control Using Stochastic Distribution Control Theory for Intersection Traffic Flow

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3. Dynamic multiple-graph spatial-temporal synchronous aggregation framework for traffic prediction in intelligent transportation systems;PeerJ Computer Science;2024-02-29

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