Research on peak transport congestion control based on convolutional neural network

Huaibing Cai; Shumeng Wang; Jingjie Wang; Fangzheng Yuan

doi:10.1117/12.2684328

14 June 2023 Research on peak transport congestion control based on convolutional neural network

Huaibing Cai, Shumeng Wang, Jingjie Wang, Fangzheng Yuan

Proceedings Volume 12708, 3rd International Conference on Internet of Things and Smart City (IoTSC 2023）; 127082V (2023) https://doi.org/10.1117/12.2684328
Event: 3rd International Conference on Internet of Things and Smart City (IoTSC 2023）, 2023, Chongqing, China

Abstract

Congestion control has been widely investigated and become an essential issue in transport network especially for peak period. However, existing methods are concentrated on congestion detection and utilize the transmission dynamics to analyze the characteristics of network congestion, which ignores the development of avoiding congestion occurrences and providing the possible solution for congestion situation. Therefore, we utilize the convolutional neural network to arrange the vehicles with limited routes to avoid the transport congestion. Initially, we collect the transport network and number of existing vehicles information about Chaozhou, which is a city in China. Subsequently, a convolutional neural network is established to dispose the congestion issue when the transport network in peak period. The neural network contains the convolution layer, data pro-processing layer, softmax to optimize the output and a full-connected layer as obtaining the final results. At last, two compared congestion control mathematical models are also simulated in same situation to evaluate our trained neural network. From our extensive experimental results, we can conclude that our proposed method can effectively avoid the occurrences of transport congestion and contain reasonable computation costs compared with existing congestion control algorithms.

Citation Download Citation

Huaibing Cai, Shumeng Wang, Jingjie Wang, and Fangzheng Yuan "Research on peak transport congestion control based on convolutional neural network", Proc. SPIE 12708, 3rd International Conference on Internet of Things and Smart City (IoTSC 2023）, 127082V (14 June 2023); https://doi.org/10.1117/12.2684328

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