Journal of Guangxi Normal University(Natural Science Edition) ›› 2022, Vol. 40 ›› Issue (2): 15-26.doi: 10.16088/j.issn.1001-6600.2021020301

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Multi-scale Prediction of Expressways' Arrival Volume of Large and Medium-sized Trucks Based on System Relevance

LIN Peiqun1*, HE Huohua1, LIN Xukun2   

  1. 1. School of Civil and Transportation, South China University of Technology, Guangzhou Guangdong 510641, China;
    2. Department of Transportation of Guangdong Province, Guangzhou Guangdong 510101, China
  • Received:2021-02-03 Revised:2021-03-23 Published:2022-05-31

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Abstract: In recent years, the proportion of large and medium-sized trucks in the traffic flow has gradually increased, and the impact on urban traffic is increasing day by day. Accurate and timely prediction of the arrival of large and medium-sized trucks is of great significance to accurate urban traffic control. To solve this problem, a multi-scale prediction method for the arrival volume of large and medium-sized trucks on expressway based on the system correlation is proposed: the spatio-temporal correlation of the flow of large and medium-sized trucks at the entrance and exit of expressway toll stations is analyzed, and a neural network model is constructed to learn the spatial weight and time weight. In each step of time, the input is fused with spatial weight and time weight, and the offset term is set to obtain the prediction result of the time step after correction.The final prediction result is obtained by summing the prediction results of each step of time. The experimental results show that the prediction accuracy of the method is 90.92%, 92.48% and 94.33% respectively at the time scales of 15, 30 and 60 mins, which is better than other models, and the practicability and effectiveness are guaranteed.

Key words: traffic flow prediction, system relevance, neural networks, expressways, large and medium-sized trucks volume

CLC Number: 

  • U491
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