Characterizing Heterogeneous Traffic Flow at a Slope Bottleneck via Cellular Automaton Model

Zhang, Futao; Qian, Yongsheng; Zeng, Junwei

doi:10.1109/tits.2022.3182352

Cited by 8 publications

(3 citation statements)

References 40 publications

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“…When examining traffic flow on a specific section of the road, several overarching factors come into play. These include the overall road infrastructure both within and outside the section being studied [88][89][90], as well as temporary factors that can be cyclic, like changes in time of day and seasons [35,91,92], or random, such as variations in weather conditions [93].…”

Section: Analysis Of the Influence Of The Dynamics Of The Pts On The ...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: Analysis Of the Influence Of The Dynamics Of The Pts On The ...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

View full text Add to dashboard Cite

show abstract

“…When examining traffic flow on a specific section of the road, several overarching factors come into play. These include the overall road infrastructure both within and outside the section being studied [60][61][62], as well as temporary factors that can be cyclic, like changes in time of day and seasons [23,63,64], or random, such as variations in weather conditions [65].…”

Section: Analysis Of the Influence Of The Dynamics Of The Pts On The ...mentioning

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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“…Therefore, many scholars actively have engaged in traffic flow modeling research to investigate the underlying causes of traffic congestion. Currently, traffic micro models [1][2][3][4][5][6][7][8][9][10][11][12][13] and macro models [14][15][16][17][18][19][20] are advantageous in investigating traffic phenomena. Especially, Natagani [21][22][23] provided original lattice models to reproduce complex traffic phenomena on a single lane and two lanes, which can demonstrate the evolution process of traffic flux, density and speed.…”

Section: Introductionmentioning

confidence: 99%

Speed limit effect during lane change in a two-lane lattice model under V2X environment

Jin 金,

Peng 彭,

Nie 聂

2024

Chinese Phys. B

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The speed limit measures are ubiquitous due to the complexity of the road environment, which can be supplied with the help of vehicle to everything (V2X) communication technology. Therefore, the influence of speed limit on traffic system will be investigated to construct a two-lane lattice model accounting for the speed limit effect during lane change process under V2X environment. Accordingly, the stability condition and the mKdV equation are closely associated with the speed limit effect through theory analysis. Moreover, the evolution of density and hysteresis loop is simulated to demonstrate the positive role of the speed limit effect on traffic stability at the cases of strong reaction intensity and high limited speed.

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Characterizing Heterogeneous Traffic Flow at a Slope Bottleneck via Cellular Automaton Model

Cited by 8 publications

References 40 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

Speed limit effect during lane change in a two-lane lattice model under V2X environment

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