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Modeling Short‐Term Highway Traffic Conflict Frequency: Integrating Interaction Effects With Stochastic Components.

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Title: Modeling Short‐Term Highway Traffic Conflict Frequency: Integrating Interaction Effects With Stochastic Components.
Authors: Sun, Hongxian1 (AUTHOR), Zheng, Xingcai2 (AUTHOR), Wang, Xiaoan1 (AUTHOR), Yin, Ruiqi1 (AUTHOR), Xu, Min1 (AUTHOR), Liu, Dan3 (AUTHOR), Cai, Jing2 (AUTHOR), Zhang, Ran2 (AUTHOR) zrshss@stu.kust.edu.cn, Qin, Yanyan (AUTHOR) qinyanyan@cqjtu.edu.cn
Source: Journal of Advanced Transportation. 4/29/2026, Vol. 2026, p1-16. 16p.
Subjects: Traffic conflicts, Random effects model, Moderation (Statistics), Zero-inflated probability distribution, Traffic flow, Traffic safety, Stochastic models
Abstract: The application of traffic conflicts in road safety assessment is increasingly favored, owing to its critical importance in conducting real‐time safety analyses and formulating proactive safety management strategies. The study aims to construct an accurate model to predict the frequency and occurrence of traffic conflicts on highways within a short time frame. Utilizing the highD dataset, we construct traffic characteristic indicators as covariates, including traffic volume (TV), speed variation coefficient (SVC), the proportion of large vehicles (PLV), lane‐to‐lane average speed difference (LASD), and front‐to‐rear vehicle average speed difference (FASD). Traffic conflict frequency, measured by the time‐to‐collision threshold of fewer than 4 s, serves as the dependent variable. The study employs zero‐inflated Poisson, zero‐inflated negative binomial, hurdle Poisson, and hurdle negative binomial models to fit conflict frequency, accounting for interaction effects, random effects, and random parameters. The findings indicate that models incorporating interaction effects yield a better fit than those not considering interaction effects. Furthermore, models considering interaction effects, random effects, and random parameters show superior fit compared to models only considering interaction effects, albeit with increased complexity under the hurdle Poisson and hurdle negative binomial distributions. These results offer valuable insights for managing and controlling highway traffic safety. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Advanced Transportation is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Modeling Short‐Term Highway Traffic Conflict Frequency: Integrating Interaction Effects With Stochastic Components.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Advanced+Transportation%22">Journal of Advanced Transportation</searchLink>. 4/29/2026, Vol. 2026, p1-16. 16p.
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  Data: <searchLink fieldCode="DE" term="%22Traffic+conflicts%22">Traffic conflicts</searchLink><br /><searchLink fieldCode="DE" term="%22Random+effects+model%22">Random effects model</searchLink><br /><searchLink fieldCode="DE" term="%22Moderation+%28Statistics%29%22">Moderation (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Zero-inflated+probability+distribution%22">Zero-inflated probability distribution</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+flow%22">Traffic flow</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+safety%22">Traffic safety</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+models%22">Stochastic models</searchLink>
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  Data: The application of traffic conflicts in road safety assessment is increasingly favored, owing to its critical importance in conducting real‐time safety analyses and formulating proactive safety management strategies. The study aims to construct an accurate model to predict the frequency and occurrence of traffic conflicts on highways within a short time frame. Utilizing the highD dataset, we construct traffic characteristic indicators as covariates, including traffic volume (TV), speed variation coefficient (SVC), the proportion of large vehicles (PLV), lane‐to‐lane average speed difference (LASD), and front‐to‐rear vehicle average speed difference (FASD). Traffic conflict frequency, measured by the time‐to‐collision threshold of fewer than 4 s, serves as the dependent variable. The study employs zero‐inflated Poisson, zero‐inflated negative binomial, hurdle Poisson, and hurdle negative binomial models to fit conflict frequency, accounting for interaction effects, random effects, and random parameters. The findings indicate that models incorporating interaction effects yield a better fit than those not considering interaction effects. Furthermore, models considering interaction effects, random effects, and random parameters show superior fit compared to models only considering interaction effects, albeit with increased complexity under the hurdle Poisson and hurdle negative binomial distributions. These results offer valuable insights for managing and controlling highway traffic safety. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Advanced Transportation is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1155/atr/1996113
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        Text: English
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        PageCount: 16
        StartPage: 1
    Subjects:
      – SubjectFull: Traffic conflicts
        Type: general
      – SubjectFull: Random effects model
        Type: general
      – SubjectFull: Moderation (Statistics)
        Type: general
      – SubjectFull: Zero-inflated probability distribution
        Type: general
      – SubjectFull: Traffic flow
        Type: general
      – SubjectFull: Traffic safety
        Type: general
      – SubjectFull: Stochastic models
        Type: general
    Titles:
      – TitleFull: Modeling Short‐Term Highway Traffic Conflict Frequency: Integrating Interaction Effects With Stochastic Components.
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            NameFull: Sun, Hongxian
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              M: 04
              Text: 4/29/2026
              Type: published
              Y: 2026
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