Calibration of Traffic Incident Simulation Models Using Field Data

Rompis, Semuel Y. R.; Habtemichael, Filmon

doi:10.31427/ijstt.2019.2.1.3

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Various calibration techniques can help bridge the gap between the simulated model and actual traffic conditions in PTS. These include direct calibration of the ODM [33][34][35][36][37], calibration of the macroscopic model based on physical processes [38][39][40], and the use of evolutionary algorithms to optimize calibration parameters [41][42][43]. Studies suggest that relying on a single traffic flow model may not capture all relevant phenomena accurately, potentially impacting simulation accuracy [13].…”

Section: Implementation Of Intelligent Transport Systems Via Simulati...mentioning

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: Implementation Of Intelligent Transport Systems Via Simulati...mentioning

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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show abstract

“…Various calibration techniques can help bridge the gap between the simulated model and actual traffic conditions in FTP. These include direct calibration of the demand model [21][22][23][24][25], calibration of the macroscopic model based on physical processes [26][27][28], and the use of evolutionary algorithms to optimize calibration parameters [29][30][31]. Studies suggest that relying on a single traffic flow model may not capture all relevant phenomena accurately, potentially impacting simulation accuracy [32].…”

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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Trafik olaylarının çevresel etkileri: İstanbul O2 (TEM) otoyolu örneği

Boz,

Özen

2024

JIENS

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Karayolu kapasitesindeki olağandışı azalmalar veya talepteki beklenmedik artışlar (örneğin, araç arızaları ve trafik kazaları) trafik olayı olarak tanımlanmaktadır. Trafikte yaşanan gecikmelerin yaklaşık %25’i bu tür olaylardan kaynaklanmaktadır. Bu olaylar, kapasiteyi doğrudan (örneğin, şeritlerin kısmen veya tamamen kapanması) ya da dolaylı olarak (sürücülerin kazayı izlemek amacıyla yavaşlaması) etkileyerek, gecikmelerin ve seyahat sürelerinin uzamasına, yakıt tüketiminin artmasına ve çevreye zararlı gaz emisyonlarının artmasına neden olmaktadır. Bu çalışmanın amacı, trafik olaylarının çevresel etkilerini incelemektir. Bu bağlamda, İstanbul O2 (TEM) Otoyolu üzerinde meydana gelen bir trafik kazasının çevresel etkileri (CO, NOx, VOC ve yakıt tüketimi) VISSIM mikro ölçekli trafik benzetim programı kullanılarak analiz edilmiştir. Beklenildiği gibi, trafikte meydana gelen olayların karayolu işletmesi performans ölçütleri üzerinde olumsuz etkileri tespit edilmiştir. Ayrıca, benzetim programı kullanılarak gerçekleştirilen bu analize dayanarak trafik olaylarının çevresel etkilerini azaltmak için çeşitli önerilerde de bulunulmuştur.

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Calibration of Traffic Incident Simulation Models Using Field Data

Cited by 3 publications

References 17 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

Trafik olaylarının çevresel etkileri: İstanbul O2 (TEM) otoyolu örneği

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