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Road traffic can be predicted by machine learning equally effectively as by complex microscopic model.

Andrzej Sroczyński1, Andrzej Czyżewski2

  • 1Multimedia Systems Department, Faculty of Electronics, Telecommunication and Informatics, Gdansk University of Technology, 80-233, Gdańsk, Poland. andrzej.sroczynski@pg.edu.pl.

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Machine learning models, including recurrent neural networks, offer real-time traffic prediction for variable message signs, outperforming traditional traffic simulators when real-world data is scarce.

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Area of Science:

  • Intelligent Transportation Systems
  • Machine Learning Applications
  • Traffic Engineering

Background:

  • High-quality real-world traffic data is often unavailable for developing traffic control solutions.
  • Software traffic simulators are typically used offline but may not meet real-time demands.
  • Existing traffic simulation methods analyze traffic at the individual vehicle level.

Purpose of the Study:

  • To develop and test machine learning models for real-time traffic prediction.
  • To compare the effectiveness of neural network models against traditional traffic simulators.
  • To assess the suitability of these models for applications like variable message signs.

Main Methods:

  • Development and testing of Long Short-Term Memory (LSTM) networks.
  • Implementation and evaluation of Gated Recurrent Unit (GRU) networks.
  • Experimentation with Stacked Autoencoder (SAE) networks.
  • Comparison with results from a microscopic traffic simulator.

Main Results:

  • Neural network algorithms demonstrate real-time processing capabilities.
  • Machine learning models show comparable or superior traffic prediction effectiveness.
  • Recurrent neural networks are suitable for real-time traffic control applications.

Conclusions:

  • Machine learning, particularly neural networks, provides a viable alternative to traditional simulators for real-time traffic management.
  • These models can effectively predict traffic conditions for immediate use, such as updating variable message signs.
  • The study highlights the potential of AI in enhancing the efficiency and responsiveness of intelligent transportation systems.