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Related Experiment Videos

Enhancing Expressway Traffic State Perception: A Novel BAS-Optimized PSO-BP Fusion Model with Tensor Completion.

Jiacheng Yin1, Xiaofei Guo1, Wei Bai2,3

  • 1School of Automobile and Transportation, Xihua University, Chengdu 610039, China.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary

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This summary is machine-generated.

This study fuses multi-source traffic data using a novel BSO-BP model, improving accuracy for intelligent expressway management. The enhanced fusion method overcomes limitations of traditional approaches, providing more reliable traffic insights.

Area of Science:

  • Intelligent Transportation Systems
  • Data Fusion
  • Machine Learning

Background:

  • Traditional single-source traffic data lacks sufficient spatial-temporal coverage and accuracy for intelligent expressways.
  • Existing data preprocessing methods struggle to capture global spatiotemporal features.
  • Conventional Particle Swarm Optimization-Backpropagation (PSO-BP) neural networks are susceptible to local optima.

Purpose of the Study:

  • To develop a robust multi-source traffic data fusion model for enhanced intelligent expressway operation.
  • To address limitations in data accuracy and spatiotemporal feature extraction.
  • To improve the global search capability and convergence stability of traffic data fusion models.

Main Methods:

  • Utilized fusion of Electronic Toll Collection-Dedicated Short Range Communication (ETC-DSRC) and RTMS microwave data.
Keywords:
BSO-BP neural networkexpresswaymulti-source data fusiontraffic flow parameter estimation

Related Experiment Videos

  • Employed the HaLRTC tensor completion algorithm for data repair and spatiotemporal correlation mining.
  • Introduced the Beetle Antennae Search (BAS) mechanism into PSO to optimize a PSO-BP neural network (BSO-BP) for data fusion.

    • Main Results:

    • The proposed BSO-BP model demonstrated significantly higher accuracy in predicting average road speed compared to single-source data and other benchmark models (BP, PSO-BP, GA-PSO-BP).
    • The fusion model effectively captured spatiotemporal correlation characteristics of traffic flow.
    • The BAS optimization improved the global search capability and convergence stability of the fusion model.

    Conclusions:

    • The BSO-BP model offers a superior approach for multi-source traffic data fusion in intelligent expressways.
    • This method enhances the accuracy and reliability of traffic state estimation.
    • The findings support more refined operation and management of intelligent expressway networks.