| contributor author | Xiong, Yingchang | |
| contributor author | Zhu, Hong | |
| contributor author | Xie, Chi | |
| contributor author | Tang, Keshuang | |
| contributor author | Sun, Fengmei | |
| contributor author | Feng, Jialong | |
| date accessioned | 2026-08-20T20:56:48Z | |
| date available | 2026-08-20T20:56:48Z | |
| date copyright | 2026/04/24 | |
| date issued | 2026 | |
| identifier other | JTEPBS.TEENG-9357.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4313713 | |
| description abstract | AbstractRecently, adaptive intersection signal control methods based on artificial intelligence
(AI) have garnered substantial research interest. As these studies progress, the practical
feasibility of AI-driven intersection control has been increasingly ...Practical ApplicationsThis study introduces a new approach to managing traffic signals in urban areas, particularly
where traditional and advanced AI-based systems coexist. With cities increasingly
adopting AI technologies for traffic control, ... | |
| publisher | American Society of Civil Engineers | |
| title | Deep Reinforcement Learning for Hybrid Traffic Control: Coordinating AI and Fixed-Time Signal Controllers in Urban Networks | |
| type | Journal Article | |
| journal volume | 152 | |
| journal issue | 7 | |
| journal title | Journal of Transportation Engineering, Part A: Systems | |
| identifier doi | 10.1061/JTEPBS.TEENG-9357 | |
| journal fristpage | 04026036-1 | |
| journal lastpage | 04026036-19 | |
| page | 19 | |
| tree | Journal of Transportation Engineering, Part A: Systems:;2026:;Volume ( 152 ):;issue: 007 | |
| contenttype | Fulltext | |