Adaptive Traffic Signal Timing Optimization Using Deep Reinforcement Learning in Urban Networks

This document please leave the flashlight that uses the extra work (DRL) for local communication. LEFTMENT Activity GRARIAR (DDPG) MATAL-Multi-multi-multi-multisgre traffic IET, QUEUE LONGS, and Distance Models. Problems the management of the controller is designed to continue with the operating sites including reductions, by maintenance operations. The Network Network architecture is designed, featuring special components and mental health regulations from cars. Procedures are used and measured two test platform and real car information from the main city of metrozopolitan from 12 intersections. Experiment that the recommended suggestions have enhanced achievement processes existing, including the suspension of 23.5% in the average attack. The latest-world of validity is completed in a 6-month-monthly basis. Using the use of electric wiring for local work and clouds managed cooperation, improve the intelligence application.

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Publication Details

Journal
Artificial Intelligence and Machine Learning Review
Published
2024-10-17
DOI
https://doi.org/10.69987/aimlr.2024.50405
Citations
5
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
1.33
Controls
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article

Adaptive Traffic Signal Timing Optimization Using Deep Reinforcement Learning in Urban Networks

Jiang Wu, Zhonghao Wu, Chunhe Ni, Shaobo Wang
5 citations
Artificial Intelligence and Machine Learning Review
Traffic Prediction and Management Techniques
1.33
article

Adaptive Traffic Signal Timing Optimization Using Deep Reinforcement Learning in Urban Networks

Jiang Wu, Zhonghao Wu, Chunhe Ni, Shaobo Wang
article en
5 citations

Abstract

This document please leave the flashlight that uses the extra work (DRL) for local communication. LEFTMENT Activity GRARIAR (DDPG) MATAL-Multi-multi-multi-multisgre traffic IET, QUEUE LONGS, and Distance Models. Problems the management of the controller is designed to continue with the operating sites including reductions, by maintenance operations. The Network Network architecture is designed, featuring special components and mental health regulations from cars. Procedures are used and measured two test platform and real car information from the main city of metrozopolitan from 12 intersections. Experiment that the recommended suggestions have enhanced achievement processes existing, including the suspension of 23.5% in the average attack. The latest-world of validity is completed in a 6-month-monthly basis. Using the use of electric wiring for local work and clouds managed cooperation, improve the intelligence application.

Artificial Intelligence and Machine Learning ReviewVol. 5(4)
University of Southern California (US), The University of Texas at Dallas (US), Santa Clara University (US), New York University (US)
Sustainable cities and communities
Openalex Percentile: Top 20%
Traffic Prediction and Management Techniques
1.33
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