Vol. 1 No. 1 (2024)
Volume 1, Number 1

Articles

Short-term Traffic Flow Prediction：A Method of MEA-LSTM Model Based on Chaotic Characteristics Analysis

Published 30.11.2024

  • Zixuan Zhang
    • ,
  • Dongliang Zhao
    • ,
  • Yan Li
    • ,
  • Zhe Huang

Zixuan Zhang
School of Transportation Engineering, Chang’an University, Xi’an 710064, China

Dongliang Zhao
Shandong Provincial Communications Planning and Design Institute Group Co., LTD, Jinan 250002, China

Yan Li
School of Civil Aviation, Northwestern Polytechnical University, Xi'an 710072, China

Zhe Huang
Hebei Xiong'an New Area Management Committee Construction and Transportation Management Bureau, Xiong'an 070001, China

Abstract

To effectively improve the accuracy of short-term traffic flow prediction, an improved Long Short-Term Memory (LSTM) method is proposed using the Mind Evolution Algorithm (MEA). Firstly, to address the issues of abnormal and missing traffic flow data, a Neighborhood Stacked Denoising AutoEncoder (NSDAE) is used for data repair. Then, the maximum Lyapunov exponent is used to determine the chaotic characteristics. Meanwhile, based on Bayesian estimation theory, the features of three-parameter sequences are fused in high-dimensional space using phase space reconstruction technique to obtain reconstructed multi-parameter fused traffic flow data. Finally, by taking advantage of the fact that MEA can divide the data into several subpopulations for optimal search separately, a prediction model based on MEA to improve LSTM is proposed. The results show that compared to the other two traditional data restoration methods, the NSDAE has higher accuracy, with the lowest average values of RMSE, MAE, and MAPE. Through the phase space reconstruction technique, the feature fusion of three parameters of traffic flow is realized in high-dimensional space, which makes up for the insufficiency of a single time-series data that cannot comprehensively levy the characteristics of traffic flow. The MEA-LSTM model outperforms the LSTM model in terms of prediction accuracy, computational efficiency, and generalization ability, and its RMSE, MEA, and MAPE are reduced by 24.3%, 28.9%, and 30.1%, respectively.

Keywords

Short-term traffic flow prediction, intelligent transportation, chaotic characteristics, machine learning

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