On improving the regional transportation efficiency based on federated learning |
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| Institution: | 1. School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen, 518107 China;2. Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, 541004 China;3. Shenzhen Key Laboratory of Navigation and Communication Integration, Shenzhen, 518107 China;1. School of Cyber Security, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China;2. Department of Information Systems and Technology, Mid Sweden University, Sundsvall, Sweden;3. University of Sydney, Sydney, Australia;4. JD Explore Academy, Beijing, China;5. School of Information Science and Technology, Dalian Maritime University, Dalian, China;1. Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing, 163318, China;2. Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing, 163318, China;3. Sanya Offshore Oil & Gas Research Institute, Northeast Petroleum University, Sanya 572025, China;4. School of Petroleum Engineering, Northeast Petroleum University, Daqing, 163318, China;5. Key Laboratory of Enhanced Oil and Gas Recovery (Northeast Petroleum University), Ministry of Education, Daqing, 163318, China;6. School of Physics and Electronic Engineering, Northeast Petroleum University, Daqing, 163318, China;1. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, 210044, China;2. College of Automation, Chongqing University, Chongqing 400044, China;1. Engineering Research Center of Internet of Things Technology and Applications (Ministry of Education), Jiangnan University, Wuxi, Jiangsu 214122, China;2. Department of Electrical Engineering, Yeungnam University, Kyongsan, Republic of Korea |
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| Abstract: | In recent years, regional traffic congestion has become increasingly frequent, which seriously affects the safety and efficiency of urban vehicles. Therefore, traffic flow prediction methods based on artificial intelligence are widely used in traffic management. However, the existing traffic flow prediction methods need to collect raw data, which involves risks of vehicle privacy leakage. Federated learning, which shares model updates without exchanging local data, has gradually become an effective solution to achieve privacy protection. A federated learning traffic flow prediction model for regional transportation systems is proposed in this paper. At the same time, due to the emergence of highly intelligent automatic driving vehicles, a vehicle scheduling system, which can control the departure and routes of vehicles in urban regions is developed in the proposed approach. A road weight measurement method combined with real time traffic information is introduced to optimize the driving routes of vehicles to reduce the average travel time. Additionally, departure strategy, is another factor that has a great influence on traffic efficiency, but is usually ignored in the past, and is also carefully compared and studied in this paper. The numerical results illustrate that the proposed schemes can effectively improve the privacy protection ability of model updates, reduce the scheduling completion time by using the traffic flow prediction model, and realize the comparative research between departure strategies, which provides a reference for developing a safe and efficient regional transportation system. |
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