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LSTM Network Integrated with Particle Filter for Predicting the Bus Passenger Traffic.

G S Vidya1,2, V S Hari3

  • 1Department of Electronics, College of Engineering Chengannur, Kerala, India 691521.

Journal of Signal Processing Systems
|January 23, 2023
PubMed
Summary
This summary is machine-generated.

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This study introduces an integrated deep learning and Bayesian filtering model for accurate passenger traffic prediction. The model effectively forecasts bus passenger flow for improved scheduling, even with limited training data.

Area of Science:

  • Artificial Intelligence
  • Data Science
  • Transportation Engineering

Background:

  • Accurate passenger traffic prediction is crucial for efficient public transportation scheduling.
  • Traditional methods often struggle with the complex temporal and spatial dynamics of traffic data.
  • Integrating advanced machine learning with statistical filtering offers a promising approach.

Purpose of the Study:

  • To develop and validate an integrated model combining deep learning and Bayesian filtering for passenger traffic prediction.
  • To analyze temporal patterns in passenger traffic data.
  • To enhance bus scheduling efficiency through accurate short-term forecasts.

Main Methods:

  • Utilized a Long Short-Term Memory (LSTM) network for time series sequential prediction.
Keywords:
Bayesian filtersDeep LearningLSTMMarkov chainParticle filter

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  • Integrated a Particle Filter (Bayesian filtering) to capture Markovian behavior.
  • Analyzed temporal (morning, noon, post-noon) and spatial features of traffic data.
  • Statistically modeled identified temporal patterns.

    • Main Results:

    • The integrated model accurately predicted passenger flow for the next thirty days.
    • Identified distinct morning, noon, and post-noon traffic patterns.
    • Achieved a high coefficient of determination (R²) of 0.88 in predictions.
    • Demonstrated effectiveness even with small training datasets.

    Conclusions:

    • The proposed deep learning and Bayesian filtering model is effective for passenger traffic prediction.
    • The model's ability to capture temporal dynamics enhances its predictive power.
    • Accurate short-term forecasts facilitate optimized bus scheduling and resource allocation.