arXiv：cs.LG· Yousef AlSaqabi·· 6 小时前AI 评分31
基于强化学习的物联网路口交通信号控制
Reinforcement Learning-Based Traffic Signal Control for IoT-Enabled Intersections
AI 导读
一项研究用 PPO 强化学习控制器为科威特某城市路口动态分配绿灯时长，仅依赖本地观测的交通状态，无需未来需求信息或集中协调。在模拟环境中，该控制器将平均车辆延误较定时控制降低 46%、较车辆感应控制降低 34%，单车 CO2 排放减少约 23%。增益在 ±15% 需求扰动下保持，并可跨工作日与周末泛化。
正文
Abstract:Urban traffic congestion remains a persistent challenge in car-dependent cities, imposing significant economic and societal costs. Traffic signal systems are increasingly deployed as networked cyber-physical components within smart-city infrastructures, where distributed sensing and edge intelligence enable adaptive traffic management. This paper investigates reinforcement learning (RL) as an edge-intelligent approach for adaptive traffic signal operation at a signalized urban intersection in Kuwait. A Proximal Policy Optimization (PPO)-based controller is developed to dynamically allocate green-phase durations using locally observed traffic states, without relying on future demand information or centralized coordination. The controller is evaluated in a realistic simulation environment informed by real-world hourly traffic volume data from Kuwait, and is compared against both conventional fixed-time control and a vehicle-actuated controller representing the current state of practice, using average vehicle delay, queue length, and emissions as performance metrics. Under nominal conditions, the proposed controller reduces average vehicle delay by 46% relative to fixed-time control and 34% relative to actuated control, while also lowering per-vehicle CO2 emissions by approximately 23%. These performance gains persist under demand perturbations of +/-15%, generalize from weekday to weekend traffic patterns, and are corroborated by a reward function ablation; low variance across five random seeds confirms their statistical reliability. These findings demonstrate the practicality of learning-based edge traffic signal control as a building block for IoT-enabled smart-city transportation systems, and as a deployable precursor toward fully connected, Internet of Vehicles (IoV)-based urban mobility.
| Comments: | 15 pages, 7 figures, Published in IEEE Open Journal of Intelligent Transportation Systems |
| Subjects: | Systems and Control (eess.SY); Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.22108 [eess.SY] |
| (or arXiv:2606.22108v2 [eess.SY] for this version) | |
| https://doi.org/10.48550/arXiv.2606.22108 arXiv-issued DOI via DataCite |
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| Related DOI: | https://doi.org/10.1109/OJITS.2026.3739219
DOI(s) linking to related resources |
Submission history
From: Yousef AlSaqabi [view email]
[v1]
Sat, 20 Jun 2026 15:44:01 UTC (1,322 KB)
[v2]
Tue, 6 Oct 2026 21:39:54 UTC (3,993 KB)
来源：arXiv：cs.LG · arxiv.org