Version 1
: Received: 26 April 2024 / Approved: 27 April 2024 / Online: 28 April 2024 (10:58:56 CEST)
How to cite:
Lopukhova, E.; Abdulnagimov, A.; Voronkov, G.; Grakhova, E. Machine Learning-Driven Calibration of Traffic Models based on a Real-time Video Analysis. Preprints2024, 2024041799. https://doi.org/10.20944/preprints202404.1799.v1
Lopukhova, E.; Abdulnagimov, A.; Voronkov, G.; Grakhova, E. Machine Learning-Driven Calibration of Traffic Models based on a Real-time Video Analysis. Preprints 2024, 2024041799. https://doi.org/10.20944/preprints202404.1799.v1
Lopukhova, E.; Abdulnagimov, A.; Voronkov, G.; Grakhova, E. Machine Learning-Driven Calibration of Traffic Models based on a Real-time Video Analysis. Preprints2024, 2024041799. https://doi.org/10.20944/preprints202404.1799.v1
APA Style
Lopukhova, E., Abdulnagimov, A., Voronkov, G., & Grakhova, E. (2024). Machine Learning-Driven Calibration of Traffic Models based on a Real-time Video Analysis. Preprints. https://doi.org/10.20944/preprints202404.1799.v1
Chicago/Turabian Style
Lopukhova, E., Grigory Voronkov and Elizaveta Grakhova. 2024 "Machine Learning-Driven Calibration of Traffic Models based on a Real-time Video Analysis" Preprints. https://doi.org/10.20944/preprints202404.1799.v1
Abstract
Accurate traffic simulation models play a crucial role in developing intelligent transport systems that offer timely traffic information to users and efficient traffic management. However, calibrating these models to represent real-world traffic conditions accurately poses a significant challenge due to the dynamic nature of traffic flow and the limitations of traditional calibration methods. This article introduces a machine learning-based approach to calibrate macroscopic traffic simulation models using real-time traffic video stream data. By leveraging computer vision technologies to extract key traffic parameters from video streams, the approach demonstrated a notable improvement in aligning the generated data from the calibrated simulation model with car sensor data, achieving an average improvement of over 50% compared to the uncalibrated macroscopic model. Moreover, there was a substantial reduction in data drift for the machine learning model integrated into the virtual transport space using vehicle-to-everything technology, resulting in a more than fourfold decrease in the average absolute error of the model.
Keywords
Traffic simulation models; connected car; calibration; V2I; machine learning; intelligent analysis of the video stream
Subject
Engineering, Transportation Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
