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Automatic Calibration of Microscopic Traffic Simulation Models Using Artificial Neural Networks.

Rodrigo F Daguano1, Leopoldo R Yoshioka1, Marcio L Netto1

  • 1Department of Electronic Systems Engineering, Escola Politécnica da Universidade de São Paulo, São Paulo CEP 05508-010, SP, Brazil.

Sensors (Basel, Switzerland)
|November 14, 2023
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Summary

This study introduces an automated method using artificial neural networks (ANNs) to calibrate traffic simulation parameters. The approach accurately mimics real-world traffic conditions, improving urban mobility planning.

Keywords:
artificial neural networkautomatic simulationcalibrationmicrosimulationtraffic models

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Area of Science:

  • Transportation Engineering
  • Artificial Intelligence
  • Urban Planning

Background:

  • Microscopic traffic simulation models are crucial for urban mobility planning and operation.
  • Manual calibration of simulation parameters is time-consuming and lacks systematic methodology.
  • Accurate calibration is essential for effective traffic simulation to mirror real-world scenarios.

Purpose of the Study:

  • To propose and evaluate a novel methodology for the automatic calibration of traffic simulation parameters.
  • To leverage artificial neural networks (ANNs) for learning and optimizing simulation model behavior.
  • To enhance the efficiency and accuracy of traffic simulation calibration for urban mobility studies.

Main Methods:

  • Developed a methodology using Multi-Layer Perceptron (MLP) artificial neural networks trained with back-propagation.
  • Trained the MLP to learn transport network behavior and optimize simulation parameters.
  • Tested the methodology on microscopic traffic models in São Paulo, Brazil, using Wiedemann 74 and 99 psychophysical models and various back-propagation algorithms (SGD, Adam, Adagrad, etc.).

Main Results:

  • The proposed ANN-based methodology achieved accurate and efficient calibration, with correlation coefficients exceeding 0.8 for parameters with significant network effects (travel times, vehicle counts, average speeds).
  • For psychophysical parameters in the simplified Wiedemann 74 model, the correlation coefficient surpassed 0.7.
  • The system demonstrated effectiveness across different MLP configurations and back-propagation training methods.

Conclusions:

  • Artificial neural networks provide a systematic and automated approach to calibrating traffic simulation parameters.
  • This method significantly improves the ability of traffic engineers to conduct comprehensive studies on diverse future scenarios.
  • The automated calibration facilitates more robust urban mobility planning and operational strategies.