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

Sroczyński, Andrzej; Czyżewski, Andrzej

doi:10.1038/s41598-023-41902-y

Cited by 6 publications

(3 citation statements)

References 39 publications

(42 reference statements)

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“…Digital-twin, federated learning, reinforcement learning, and machine learning have been widely applied in the literature and in this Special Collection, ranging from passenger demand forecasting and the prediction of electricity consumption using traffic volume data 9 to the optimization of traffic signal controls and the evaluation of the pedestrian level of service 10 – 12 . The debate around the potential of big data analytics is lively, and how/if they will replace traditional transport modelling techniques 13 .…”

Section: Advances Toward Technology-enabled Transportmentioning

confidence: 99%

Advanced transport systems: the future is sustainable and technology-enabled

Cao,

Derrible,

Le Pira

et al. 2024

Sci Rep

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Transport has always played a major role in shaping society. By enabling or restricting the movement of people and goods, the presence or absence of transport services and infrastructure has historically been determining for cultures to connect, for knowledge to be shared, and for societies to evolve and prosper, or, in contrast, for societies to decay and fail. Since the beginning of the twenty-first century, transport has been going through a revolution worldwide. One of the primary goals for the transport sector is clear: it needs to be decarbonized and become more sustainable. At the same time, technological advances are shaping the transport sector toward smart services and societies. The Special Collection showcases some of the latest advances in research towards sustainable and technology-enabled transport.

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Section: Advances Toward Technology-enabled Transportmentioning

confidence: 99%

Advanced transport systems: the future is sustainable and technology-enabled

Cao,

Derrible,

Le Pira

et al. 2024

Sci Rep

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show abstract

“…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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“…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

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

Cited by 6 publications

References 39 publications

Advanced transport systems: the future is sustainable and technology-enabled

Advanced transport systems: the future is sustainable and technology-enabled

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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