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Learning based Traffic Signal Control Algorithms with Neighborhood Information Sharing: An Application for Sustainable Mobility
| 题名： | Learning based Traffic Signal Control Algorithms with Neighborhood Information Sharing: An Application for Sustainable Mobility |
| 正文语种： | 英文 |
| 作者： | H. M. Abdul Aziz, Ph.D.; Feng Zhu, Ph.D.; Satish V. Ukkusuri, Ph.D. Professor |
| 作者单位： | 1Urban Dynamics Institute, Oak Ridge National Laboratory;Lyles School of Civil Engineering, Purdue University;Lyles School of Civil Engineering, Purdue University |
| 关键词： | Vehicular Emissions; Connected and Automated Vehicles; Traffic Signal Control; Sustainable Transportation. |
| 摘要： | This research applies R-Markov Average Reward Technique based reinforcement learning (RL) algorithm, namely RMART, for vehicular signal control problem leveraging information sharing among signal controllers in connected vehicle environment. We implemented the algorithm in a network of 18 signalized intersections and compare the performance of RMART with fixed, adaptive, and variants of the RL schemes. Results show significant improvement in system performance for RMART algorithm with information sharing over both traditional fixed signal timing plans and real time adaptive control schemes. The comparison with reinforcement learning algorithms including Q learning and SARSA indicate that RMART performs better at higher congestion levels. Further, a multi-reward structure is proposed that dynamically adjusts the reward function with varying congestion states at the intersection. Finally, the results from test networks show significant reduction in emissions (CO, CO2, NOx, VOC, PM10) when RL algorithms are implemented compared to fixed signal timings and adaptive schemes. |
| 出版年： | 2018 |
| 期刊名称： | Journal of Intelligent Transportation Systems Technology Planning and Operations |
| 卷： | 22 |
| 期： | 1 |
| 页码： | 40-52 |
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