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<p>Intelligent transportation systems increasingly require accurate speed predictions to guide, manage, and control traffic. Traffic speed prediction is a significant challenge in traffic systems and is critical for efficient traffic management. However, speed data are often incomplete, noisy, and subject to measurement errors, which affect prediction accuracy. This paper introduces an innovative method for predicting speed. This approach involves utilizing the genetic algorithm to optimize the hyperparameters of a convolutional neural network model. By doing so, we aim to enhance the model’s performance and mitigate the issue of overfitting. The genetic algorithm simulates the natural selection process, in which the fittest individuals are selected and combined to create the next generation. We employ a long short-term memory network to determine the window size parameter through the genetic algorithm. Additionally, we utilize a convolutional neural network with three convolutional layers, three pooling layers, and fully connected layers to extract the temporal variations in speed. We obtain the prediction output by performing result-level fusion with the optimal hyperparameters. The proposed approach demonstrated superior performance to the other benchmarks, achieving the highest prediction accuracy and the lowest prediction error. The Nash-Sutcliff index value of the proposed model is 90.3% in the best-case scenario. Here, this approach helps improve efficiency and reduce computational burdens.</p>

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Improvement of speed prediction for an urban network under GA-LSTM-CNN method

  • 期刊论文

  • 发表日期：

    2025年6月11日

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  • Bae Sang Hoon,
  • Duy Tran Quang,
  • Hung Tuan Trinh

摘要

Intelligent transportation systems increasingly require accurate speed predictions to guide, manage, and control traffic. Traffic speed prediction is a significant challenge in traffic systems and is critical for efficient traffic management. However, speed data are often incomplete, noisy, and subject to measurement errors, which affect prediction accuracy. This paper introduces an innovative method for predicting speed. This approach involves utilizing the genetic algorithm to optimize the hyperparameters of a convolutional neural network model. By doing so, we aim to enhance the model’s performance and mitigate the issue of overfitting. The genetic algorithm simulates the natural selection process, in which the fittest individuals are selected and combined to create the next generation. We employ a long short-term memory network to determine the window size parameter through the genetic algorithm. Additionally, we utilize a convolutional neural network with three convolutional layers, three pooling layers, and fully connected layers to extract the temporal variations in speed. We obtain the prediction output by performing result-level fusion with the optimal hyperparameters. The proposed approach demonstrated superior performance to the other benchmarks, achieving the highest prediction accuracy and the lowest prediction error. The Nash-Sutcliff index value of the proposed model is 90.3% in the best-case scenario. Here, this approach helps improve efficiency and reduce computational burdens.