Differentiable Hybrid Traffic Simulation

Son, Sanghyun; Qiao, Yi-Ling; Sewall, Jason

doi:10.1145/3550454.3555492

Cited by 9 publications

(3 citation statements)

References 31 publications

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“…ML for big data analysis enables the effective development and calibration of SMTFs. Integrating macroscopic modeling with ML techniques can enhance the accuracy of traffic flow modeling by improving prediction accuracy and adaptability to changing traffic conditions [65][66][67]. This hybrid approach can provide greater flexibility to changing traffic patterns by combining the overall traffic flow dynamics captured by macroscopic models with ML's ability to handle dynamic changes effectively.…”

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

“…This amalgamation ensures a balance between a holistic view and detailed insights. Multilevel or multi-resolution methods are typically employed in the construction of mesoscopic models, with examples including the three-tiered hybrid model (e.g., Storani et al, 2021;Jiang et al, 2020) and the multi-resolution hybrid simulation system (Son et al, 2022).…”

Section: Traffic Modelmentioning

confidence: 99%

Effect of Dynamic Safety Distance of Heterogeneous Traffic Flows on Ship Traffic Efficiency: A Prediction and Simulation Approach

Liu,

Zhang

et al. 2023

Preprint

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“…Another way to enhance simulation accuracy is by integrating macroscopic modeling with ML techniques to improve prediction accuracy and adaptability to changing traffic conditions [38][39][40]. This hybrid approach can provide greater flexibility to changing traffic patterns by combining the overall traffic flow dynamics captured by macroscopic models with ML's ability to handle dynamic changes effectively.…”

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

View full text Add to dashboard Cite

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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Differentiable Hybrid Traffic Simulation

Cited by 9 publications

References 31 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

Effect of Dynamic Safety Distance of Heterogeneous Traffic Flows on Ship Traffic Efficiency: A Prediction and Simulation Approach

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

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