Combining Traffic Assignment and Traffic Signal Control for Online Traffic Flow Optimization

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@InProceedings{Liao:2022:ICONIP,

• author = "Xiao-Cheng Liao and Wen-Jin Qiu and Feng-Feng Wei and Wei-Neng Chen",
• title = "Combining Traffic Assignment and Traffic Signal Control for Online Traffic Flow Optimization",
• booktitle = "29th International Conference on Neural Information Processing, ICONIP 2022",
• year = "2022",
• editor = "Mohammad Tanveer and Sonali Agarwal and Seiichi Ozawa and Asif Ekbal and Adam Jatowt",
• volume = "1793",
• series = "CCIS",
• pages = "150--163",
• address = "Virtual Event",
• month = nov # " 22-26",
• publisher = "Springer Nature",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-981-99-1645-0",
• DOI = "doi:10.1007/978-981-99-1645-0_13",
• abstract = "With the continuous development of urbanization, traffic congestion has become a key problem that plagues many large cities around the world. As new information technologies like the Internet of Things and the mobile Internet develop, the interconnection between vehicles and road facilities provides a new mechanism to improve transportation efficiency. In this paper, we adopt the mechanism of vehicle-road coordination, and propose a new dynamic traffic flow optimization approach that combines the traffic assignment method and traffic signal control method together. For traffic assignment, a gene expression programming (GEP) based online navigation algorithm is proposed to generate a generalized navigation rule for the vehicles on the road network. Each vehicle can dynamically select an appropriate route for itself through the navigation rule based on its own states and information about the nearby road network. For traffic signal control, the Maximum Throughput Control (MTC) method is adopted. MTC checks the states of the intersections periodically and greedily takes the action that maximum the throughput of the intersections. By combining these two methods, the vehicle-road coordination mechanism can significantly improve the efficiency of city traffic flow optimization. The experimental results yielded based on the CityFlow simulator verify the effectiveness of the proposed approach.",
• notes = "South China University of Technology, Guangzhou, China",

}

Genetic Programming entries for Xiao-Cheng Liao Wen-Jin Qiu Feng-Feng Wei Wei-Neng Chen

Citations