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contributor authorXiong, Yingchang
contributor authorZhu, Hong
contributor authorXie, Chi
contributor authorTang, Keshuang
contributor authorSun, Fengmei
contributor authorFeng, Jialong
date accessioned2026-08-20T20:56:48Z
date available2026-08-20T20:56:48Z
date copyright2026/04/24
date issued2026
identifier otherJTEPBS.TEENG-9357.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313713
description abstractAbstractRecently, 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, ...
publisherAmerican Society of Civil Engineers
titleDeep Reinforcement Learning for Hybrid Traffic Control: Coordinating AI and Fixed-Time Signal Controllers in Urban Networks
typeJournal Article
journal volume152
journal issue7
journal titleJournal of Transportation Engineering, Part A: Systems
identifier doi10.1061/JTEPBS.TEENG-9357
journal fristpage04026036-1
journal lastpage04026036-19
page19
treeJournal of Transportation Engineering, Part A: Systems:;2026:;Volume ( 152 ):;issue: 007
contenttypeFulltext


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