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.
Authors
- Jiang Wu (ORCID: https://orcid.org/0000-0002-6353-8890)
- Zhonghao Wu (ORCID: https://orcid.org/0000-0001-8918-1976)
- Chunhe Ni
- Shaobo Wang
Institutions
- University of Southern California (US)
- The University of Texas at Dallas (US)
- Santa Clara University (US)
- New York University (US)
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