Abstract
Large-scale personnel vehicles choose and update the optimal path in real time under congested scenarios, which is of great significance for people to travel and balance traffic network traffic. Based on the regularity of people’s work, it is important to make full use of traffic history data flow to establish optimal path planning. Our work studies the road network traffic congestion model based on historical and real-time traffic data flow to predict the roads that may be congested. Through the clustered queue communication mechanism and queue-based shunting, our work provides real-time optimal path planning for large-scale vehicles at the same time, and uniform road network traffic capacity. Our work simulated in the UESTC scenario to verify the improvement our work offers and the future potential performance.
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Du, M., Lin, S., Luo, C., Zhou, L., Yang, H. (2020). Dynamic Path Planning Method Based on Cluster Queuing Communication in VANET. In: Hsu, CH., Kallel, S., Lan, KC., Zheng, Z. (eds) Internet of Vehicles. Technologies and Services Toward Smart Cities. IOV 2019. Lecture Notes in Computer Science(), vol 11894. Springer, Cham. https://doi.org/10.1007/978-3-030-38651-1_3
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DOI: https://doi.org/10.1007/978-3-030-38651-1_3
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