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Short-Term Prediction of Traffic Flow Based on the Comprehensive Cloud Model

Author

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  • Jianhua Dong

    (School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China)

Abstract

Short-term traffic flow prediction plays a crucial role in transportation systems by describing the time evolution of traffic flow over short periods, such as seconds, minutes, or hours. It helps people make informed decisions about their routes to avoid congested areas and enables traffic management departments to quickly adjust road capacities and implement effective traffic management strategies. In recent years, numerous studies have been conducted in this area. However, there is a significant gap in research regarding the uncertainty of short-term traffic flow, which negatively impacts the accuracy and robustness of traffic flow prediction models. In this paper, we propose a novel comprehensive entropy-cloud model that includes two algorithms: the Fused Cloud Model Inference based on DS Evidence Theory (FCMI-DS) and the Cloud Model Inference and Prediction based on Compensation Mechanism (CMICM). These algorithms are designed to address the short-term traffic flow prediction problem. By utilizing the cloud model of historical flow data to guide future short-term predictions, our approach improves prediction accuracy and stability. Additionally, we provide relevant mathematical proofs to support our methodology.

Suggested Citation

  • Jianhua Dong, 2025. "Short-Term Prediction of Traffic Flow Based on the Comprehensive Cloud Model," Mathematics, MDPI, vol. 13(4), pages 1-19, February.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:4:p:658-:d:1592898
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    References listed on IDEAS

    as
    1. Wencheng Huang & Bin Shuai, 2017. "Using Improved Entropy-Cloud Model to Select High-Speed Railway Express Freight Train Service Sites," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-13, November.
    2. Queen, Catriona M. & Albers, Casper J., 2009. "Intervention and Causality: Forecasting Traffic Flows Using a Dynamic Bayesian Network," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 669-681.
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