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Báo cáo hóa học: " Research Article Multiobjective Reinforcement Learning for Traffic Signal Control Using Vehicular Ad Hoc Network"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: IResearch Article Multiobjective Reinforcement Learning for Traffic Signal Control Using Vehicular Ad Hoc Network | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 724035 7 pages doi 10.1155 2010 724035 Research Article Multiobjective Reinforcement Learning for Traffic Signal Control Using Vehicular Ad Hoc Network Duan Houli Li Zhiheng and Zhang Yi Department of Automation Tsinghua University Beijing 100084 China Correspondence should be addressed to Duan Houli duanhouli@gmail.com Received 1 December 2009 Accepted 5 September 2010 Academic Editor Hossein Pishro-Nik Copyright 2010 Duan Houli et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. We propose a new multiobjective control algorithm based on reinforcement learning for urban traffic signal control named multi-RL. A multiagent structure is used to describe the traffic system. A vehicular ad hoc network is used for the data exchange among agents. A reinforcement learning algorithm is applied to predict the overall value of the optimization objective given vehicles states. The policy which minimizes the cumulative value of the optimization objective is regarded as the optimal one. In order to make the method adaptive to various traffic conditions we also introduce a multiobjective control scheme in which the optimization objective is selected adaptively to real-time traffic states. The optimization objectives include the vehicle stops the average waiting time and the maximum queue length of the next intersection. In addition we also accommodate a priority control to the buses and the emergency vehicles through our model. The simulation results indicated that our algorithm could perform more efficiently than traditional traffic light control methods. 1. Introduction Increasing traffic congestion over the road networks makes the development of more intelligent and efficient traffic control systems an urgent .