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With the rapid development of motorways, the influence of weather on traffic is gradually significant. In this paper, we carry out principal component analysis for the intrinsic connection between weather factors and highway traffic parameters, construct a genetic algorithm integrating weather factors, optimize the Genetic Algorithm-Long Short-Term Memory Network (GA-LSTM) model, carry out a sensitivity test, and compare it with other prediction models. Comparison and analysis. The experiments show that the GA-LSTM model performs optimally in all indicators, and reduces 81% and 42% of the MRE indicators compared with the convolutional neural network and the long short-term memory network, which can effectively predict the high-speed short-term traffic data under the coupling of weather factors, and provide a scientific early warning means for the traffic department.

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Traffic Data Prediction of Motorways by GA-LSTM Under Weather Factor Coupling

  • 会议论文

  • 发表日期：

    2026年

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  • Shouqiang Xue,
  • Hongwu Wang,
  • Weichuan Yin,
  • Tao Wang,
  • Changbin Ying

摘要

With the rapid development of motorways, the influence of weather on traffic is gradually significant. In this paper, we carry out principal component analysis for the intrinsic connection between weather factors and highway traffic parameters, construct a genetic algorithm integrating weather factors, optimize the Genetic Algorithm-Long Short-Term Memory Network (GA-LSTM) model, carry out a sensitivity test, and compare it with other prediction models. Comparison and analysis. The experiments show that the GA-LSTM model performs optimally in all indicators, and reduces 81% and 42% of the MRE indicators compared with the convolutional neural network and the long short-term memory network, which can effectively predict the high-speed short-term traffic data under the coupling of weather factors, and provide a scientific early warning means for the traffic department.