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Journal of Intelligent and Connected Vehicles 2022, 5(3): 149-156 https://doi.org/10.1108/JICV-01-2022-0004
Research paper | Open Access | Issue | Published: 27 July 2022

Time headway distribution analysis of naturalistic road users based on aerial datasets

Show Author's Information Ruilin Yu,  Yuxin Zhang( ),  Luyao Wang,  Xinyi Du
Jilin University, Changchun, China

This paper forms part of a special section "Intelligent Safety for Intelligent and Connected", guest edited by Jun Li.

Keywords:
Goodness-of-fit, Adaptive parameter, Distribution model, Road user behavior, THW
Cite this article:
Yu R, Zhang Y, Wang L, et al. Time headway distribution analysis of naturalistic road users based on aerial datasets. Journal of Intelligent and Connected Vehicles, 2022, 5(3): 149-156. https://doi.org/10.1108/JICV-01-2022-0004
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Abstract Full text About this article
Purpose

Time headway (THW) is an essential parameter in traffic safety and is used as a typical control variable by many vehicle control algorithms, especially in safety-critical ADAS and automated driving systems. However, due to the randomness of human drivers, THW cannot be accurately represented, affecting scholars’ more profound research.

Design/methodology/approach

In this work, two data sets are used as the experimental data to calculate the goodness-of-fit of 18 commonly used distribution models of THW to select the best distribution model. Subsequently, the characteristic parameters of traffic flow are extracted from the data set, and three variables with higher importance are extracted using the random forest model. Combining the best distribution model parameters of the data set, this study obtained a distribution model with adaptive parameters, and its performance and applicability are verified.

Findings

In this work, two data sets are used as the experimental data to calculate the goodness-of-fit of 18 commonly used distribution models of THW to select the best distribution model. Subsequently, the characteristic parameters of traffic flow are extracted from the data set, and three variables with higher importance are extracted using the random forest model. Combining the best distribution model parameters of the data set, this study obtained a distribution model with adaptive parameters, and its performance and applicability are verified.

Originality/value

The results show that the proposed model has a 62.7% performance improvement over the distribution model with fixed parameters. Moreover, the parameter function of the distribution model can be regarded as a quantitative analysis of the degree of influence of the traffic flow state on THW.


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About this article

Time headway distribution analysis of naturalistic road users based on aerial datasets

Show Author's information Ruilin Yu, Yuxin Zhang( ), Luyao Wang, Xinyi Du
Jilin University, Changchun, China

This paper forms part of a special section "Intelligent Safety for Intelligent and Connected", guest edited by Jun Li.

Abstract

Purpose

Time headway (THW) is an essential parameter in traffic safety and is used as a typical control variable by many vehicle control algorithms, especially in safety-critical ADAS and automated driving systems. However, due to the randomness of human drivers, THW cannot be accurately represented, affecting scholars’ more profound research.

Design/methodology/approach

In this work, two data sets are used as the experimental data to calculate the goodness-of-fit of 18 commonly used distribution models of THW to select the best distribution model. Subsequently, the characteristic parameters of traffic flow are extracted from the data set, and three variables with higher importance are extracted using the random forest model. Combining the best distribution model parameters of the data set, this study obtained a distribution model with adaptive parameters, and its performance and applicability are verified.

Findings

In this work, two data sets are used as the experimental data to calculate the goodness-of-fit of 18 commonly used distribution models of THW to select the best distribution model. Subsequently, the characteristic parameters of traffic flow are extracted from the data set, and three variables with higher importance are extracted using the random forest model. Combining the best distribution model parameters of the data set, this study obtained a distribution model with adaptive parameters, and its performance and applicability are verified.

Originality/value

The results show that the proposed model has a 62.7% performance improvement over the distribution model with fixed parameters. Moreover, the parameter function of the distribution model can be regarded as a quantitative analysis of the degree of influence of the traffic flow state on THW.

Keywords: Goodness-of-fit, Adaptive parameter, Distribution model, Road user behavior, THW

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Publication history
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Publication history

Received: 28 January 2022
Revised: 12 May 2022
Accepted: 14 May 2022
Published: 27 July 2022
Issue date: October 2022

Copyright

© 2022 Ruilin Yu, Yuxin Zhang, Luyao Wang and Xinyi Du. Published in Journal of Intelligent and Connected Vehicles. Published by Emerald Publishing Limited.

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This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode

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