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Traffic Flow Prediction in 5G-Enabled Intelligent Transportation Systems Using Parameter Optimization and Adaptive

Hanh Hong-Phuc Vo1, Thuan Minh Nguyen1, Khoi Anh Bui1

  • 1Department of Electronic Engineering, Soongsil University, Seoul 06978, Republic of Korea.

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This study introduces FVMD-WOA-GA for better traffic flow prediction in 5G systems. The hybrid method improves accuracy and efficiency for intelligent transportation.

Keywords:
fast variation mode decompositiongenetic algorithmparameter optimizationtraffic flowwhale optimization algorithm

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Area of Science:

  • Intelligent Transportation Systems
  • Traffic Flow Prediction
  • Hybrid Machine Learning Models

Background:

  • Accurate traffic flow prediction is crucial for efficient transportation management in 5G-enabled intelligent transportation systems.
  • Existing methods often struggle to capture complex temporal dependencies and optimize predictive models effectively.
  • The integration of advanced decomposition and optimization techniques is needed to enhance prediction accuracy.

Purpose of the Study:

  • To propose and validate a novel hybrid method, FVMD-WOA-GA, for improving traffic flow prediction accuracy.
  • To enhance the performance of predictive models by systematically decomposing traffic data and optimizing model selection.
  • To demonstrate the method's effectiveness in reducing prediction errors and inference time for intelligent transportation systems.

Main Methods:

  • Fast Variational Mode Decomposition (FVMD) for data decomposition.
  • Whale Optimization Algorithm (WOA) and Genetic Algorithm (GA) for optimizing predictive models.
  • Utilizing Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Bidirectional GRU (BiGRU) as predictive models.

Main Results:

  • Achieved Root Mean Squared Errors (RMSEs) of 152.43 and 7.91 on two real-world traffic datasets.
  • Demonstrated significant prediction accuracy improvements of 3.44% and 12.87% compared to existing methods.
  • Validated the method's efficacy in reducing inference time and enhancing system adaptability.

Conclusions:

  • The FVMD-WOA-GA hybrid method significantly enhances traffic flow prediction accuracy in 5G-enabled intelligent transportation systems.
  • The systematic multi-stage approach of decomposition, optimization, and model selection leads to superior predictive performance.
  • The proposed method offers a promising solution for more efficient and adaptive traffic management.