Random Coefficient Models for Work Zone Headway Distribution

Weng, Jinxian; Gan, Xiafan; Du, Gang

doi:10.1061/jtepbs.0000268

Cited by 15 publications

(3 citation statements)

References 14 publications

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“…3) For Beijing West Road, except at 4, 5, 9, 11, 14, 15, 17, 20, 21, and 24 s, the best goodness-of-fit of the model is established under four states at other time headway. At 4,5,9,11,14,15,17,20,21, and 24 s, the absolute values of absolute error under two states are almost the smallest. The performance of the model established under four states is slightly better than that under two states, and much better than that under three states.…”

Section: ) Effect Of Traffic Statementioning

confidence: 95%

“…The introduction of headway distribution models has made it easy to describe uncertainty in traffic flow [2]. For example, traffic rate can be predicted based on the relationship between traffic rate and time headway by using time headway distribution according to probability theory [4], [5]. Based on traffic rate, traffic managers can smooth traffic stream, reduce traffic congestion, design traffic signal timing, and evaluate passenger satisfaction [5]- [8].…”

Section: Introductionmentioning

confidence: 99%

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An Improved Mixed Distribution Model of Time Headway for Urban Roads Based on a New Traffic State Classification Method

Zhou²,

Xing

et al. 2021

IEEE Access

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The Mixed distribution model of time headway plays an important role in Intelligent Transportation System. A more accurate improved model based on a new traffic state classification method and correlation coefficient method was established in this study. Upon analyzing traffic data from three unban roads in Nanjing, China from December 8, 2017 to April 24, 2018 in clear days free of fogs and haze, it was found that 1) the improved model dwarfs the other models, for it boasts higher goodness-of-fit and it represents the only one to successfully pass the Chi-square test. 2) the improved model shows better performance in application because the relative errors between the field traffic rate and calculated traffic rate obtained from the improved model are the smallest. 3) the goodness-of-fit of the improved model is affected by traffic state, and the fitting precision of the improved model under four states is the best. Thus, it is hard to tell performance of which model established under two states or three-state model is better. 4) correlation coefficients between time headway and the absolute value of relative speed make the improved Mixed model more fitted and precise. The improved model proposed in this study is a more accurate time headway distribution model.

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Section: ) Effect Of Traffic Statementioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

An Improved Mixed Distribution Model of Time Headway for Urban Roads Based on a New Traffic State Classification Method

Zhou²,

Xing

et al. 2021

IEEE Access

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show abstract

“…erefore, for the analysis of crash-related data, it is necessary to use a model that can explain unobserved heterogeneity. To address the issue, previous researchers used other, more promising approaches: several statistical and econometric methods are available to account for unobserved heterogeneity [26][27][28][29]. In many recent studies, mixed logit models have been used to account for unobserved heterogeneity of variables by applying individual-specific coefficients to standard logit models [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Factors Affecting Crash Involvement of Commercial Vehicle Drivers: Evaluation of Commercial Vehicle Drivers’ Characteristics in South Korea

Lee

Yeo

Yun

et al. 2020

Journal of Advanced Transportation

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The aim of this study was to evaluate the effects of driver-related factors on crash involvement of four different types of commercial vehicles—express buses, local buses, taxis, and trucks—and to compare outcomes across types. Previous studies on commercial vehicle crashes have generally been focused on a single type of commercial vehicle; however, the characteristics of drivers as factors affecting crashes vary widely across types of commercial vehicles as well as across study sites. This underscores the need for comparative analysis between different types of commercial vehicles that operate in similar environments. Toward these ends, we analyzed 627,594 commercial vehicle driver records in South Korea using a mixed logit model able to address unobserved heterogeneity in crash-related data. The estimated outcomes showed that driver-related factors have common effects on crash involvement: greater experience had a positive effect (diminished driver crash involvement), while traffic violations, job change, and previous crash involvement had negative effects. However, the magnitude of the effects and heterogeneity varied across different types of commercial vehicles. The findings support the contention that the safety management policy of commercial drivers needs to be set differently according to the vehicle type. Furthermore, the variables in this study can be used as promising predictors to quantify potential crash involvement of commercial vehicles. Using these variables, it is possible to proactively identify groups of accident-prone commercial vehicle drivers and to implement effective measures to reduce their involvement in crashes.

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Investigation of multiple-presence factor for traffic loads on road-rail bridges based on a novel extreme value analysis approach

Dai

2022

Structural Safety

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Random Coefficient Models for Work Zone Headway Distribution

Cited by 15 publications

References 14 publications

An Improved Mixed Distribution Model of Time Headway for Urban Roads Based on a New Traffic State Classification Method

An Improved Mixed Distribution Model of Time Headway for Urban Roads Based on a New Traffic State Classification Method

Factors Affecting Crash Involvement of Commercial Vehicle Drivers: Evaluation of Commercial Vehicle Drivers’ Characteristics in South Korea

Investigation of multiple-presence factor for traffic loads on road-rail bridges based on a novel extreme value analysis approach

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