Learning to Calibrate Hybrid Hyperparameters: a Study on Traffic Simulation

Xu, Wanpeng; Wei, Hua

doi:10.1145/3573900.3591113

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…In [69], it is demonstrated that ML can be helpful in dynamically calibrating parameters in macroscopic traffic flow models. For instance, ML can generate a set of hyperparameters for calibration through generative adversarial simulation learning.…”

Section: Machine Learning For Intelligent Transport System Technologiesmentioning

confidence: 99%

Machine Learning-Driven Calibration of Traffic Models Based on a Real-Time Video Analysis

Lopukhova,

Abdulnagimov,

Voronkov

et al. 2024

Applied Sciences

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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. The proposed method for creating and calibrating a traffic simulation model has significantly improved the statistical correspondence between the generated vehicle characteristics and real data about cars on the simulated road section. The correspondence has increased from 37% to 73%. Machine learning models trained on generated data and tested on real data show improved accuracy rates. Mean absolute error, mean square error, and mean absolute percentage error decreased by more than two orders of magnitude. The coefficient of determination has also increased, approaching 1. This method eliminates the need to deploy wireless sensor networks, which can reduce the cost of implementing intelligent transport systems.

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Section: Machine Learning For Intelligent Transport System Technologiesmentioning

confidence: 99%

Machine Learning-Driven Calibration of Traffic Models Based on a Real-Time Video Analysis

Lopukhova,

Abdulnagimov,

Voronkov

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…In [42], it is demonstrated that ML can be helpful in dynamically calibrating parameters in macroscopic traffic flow models. For instance, ML can generate a set of hyperparameters for calibration through generative adversarial simulation learning.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Driven Calibration of Traffic Models based on a Real-time Video Analysis

Lopukhova,

Abdulnagimov,

Voronkov

et al. 2024

Preprint

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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.

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Learning to Calibrate Hybrid Hyperparameters: a Study on Traffic Simulation

Cited by 2 publications

References 5 publications

Machine Learning-Driven Calibration of Traffic Models Based on a Real-Time Video Analysis

Machine Learning-Driven Calibration of Traffic Models Based on a Real-Time Video Analysis

Machine Learning-Driven Calibration of Traffic Models based on a Real-time Video Analysis

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