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Forecasting Short-Term Traffic Flow by Fuzzy Wavelet Neural Network with Parameters Optimized by Biogeography-Based

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This study introduces an improved fuzzy wavelet neural network (FWNN) for short-term traffic flow forecasting. The enhanced model demonstrates superior accuracy in predicting traffic patterns compared to other methods.

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Area of Science:

  • Intelligent Transportation Systems
  • Artificial Intelligence
  • Data Science

Background:

  • Accurate short-term traffic flow forecasting is crucial for intelligent transportation systems.
  • Traffic prediction impacts traveler behavior, congestion, fuel consumption, and accident risks.
  • Existing methods require enhancement for improved accuracy and efficiency.

Purpose of the Study:

  • To propose a novel Fuzzy Wavelet Neural Network (FWNN) model for short-term traffic flow forecasting.
  • To enhance the Biogeography-Based Optimization (BBO) algorithm for improved training efficiency.
  • To validate the proposed FWNN model against other forecasting approaches.

Main Methods:

  • Development of a Fuzzy Wavelet Neural Network (FWNN) integrating fuzzy logic, wavelet transform, and neural networks.
  • Enhancement of the Biogeography-Based Optimization (BBO) algorithm using ring topology and Powell's method.
  • Comparative analysis using Root-Mean-Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and correlation coefficient (R).

Main Results:

  • The proposed FWNN model, trained with the improved BBO algorithm, showed superior performance in short-term traffic flow forecasting.
  • The enhanced BBO algorithm demonstrated increased exploration capability and faster convergence.
  • The FWNN model achieved lower RMSE and MAPE, and a higher correlation coefficient (R) compared to ANN, FWNN, and WNN models.

Conclusions:

  • The Fuzzy Wavelet Neural Network (FWNN) model trained by an improved Biogeography-Based Optimization (BBO) algorithm is an effective approach for short-term traffic flow forecasting.
  • The integration of fuzzy logic, wavelet transform, and neural networks, optimized by an enhanced heuristic algorithm, provides a robust prediction tool.
  • The FWNN model significantly outperforms traditional methods, offering a more accurate and reliable solution for intelligent transportation systems.