Examination of factors associated with fault status and injury severity in intersection-related rear-end crashes: Application of binary and bivariate ordered probit models

Russo, Brendan J.; Yu, Fan; Smaglik, Edward J.

doi:10.1016/j.ssci.2023.106187

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article4

Preprint1

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 6 publications

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Investigating vehicle-vehicle and vehicle–pedestrian crash severity at street intersections with the latent class parameterized correlation bivariate generalized ordered probit

Fu,

2024

Accident Analysis & Prevention

View full text Add to dashboard Cite

Investigating vehicle-vehicle and vehicle–pedestrian crash severity at street intersections with the latent class parameterized correlation bivariate generalized ordered probit

Fu,

2024

Accident Analysis & Prevention

View full text Add to dashboard Cite

Special issue “RSS2022 Conference: Advanced Road Safety Analyses”

Yannis,

Papadimitriou

2024

Safety Science

View full text Add to dashboard Cite

Modeling and Analysis of Microscopic Risk Avoidance Behavior of Homogeneous Driver Groups Under Risk Scenarios

Zheng,

Li,

Ding

et al. 2024

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

To cope with the increasing number of road traffic crashes, it is critical to develop a driver assistance system that can provide early warning for vehicle collisions and control the vehicle at critical moments. However, to achieve this function, the driver assistance system must proactively understand drivers’ preferences and predict their risk avoidance behavior in risk scenarios, an area that currently lacks sufficient research. To address this issue, this study proposes a method for modeling microscopic risk avoidance behavior for homogeneous groups of drivers. Firstly, the risk field theory is established to achieve the basic driving risk assessment. Subsequently, a macro–micro collision-tendency probability calculation model is constructed to correct the basic driving risk values and obtain more accurate risk assessment results. Finally, a risk avoidance behavior model is developed by combining drivers’ risk response behavior and the psychology of desired speed pursuit. This study uses natural driving data for model validation. The results imply that the risk assessment indicator proposed in this study can reflect the driving risk under different risk phases. The risk avoidance behavior model accurately identifies vehicle acceleration fluctuations and matches drivers’ avoidance motivation in risk scenarios. In addition, the model parameters calibration results reveal significant differences among different driving groups; for example, risk perception and desired speed. This study aims to deepen researchers’ understanding of drivers’ risk avoidance behavior for designing driver assistance systems and road safety management.

show abstract

Examination of factors associated with fault status and injury severity in intersection-related rear-end crashes: Application of binary and bivariate ordered probit models

Cited by 6 publications

References 8 publications

Investigating vehicle-vehicle and vehicle–pedestrian crash severity at street intersections with the latent class parameterized correlation bivariate generalized ordered probit

Investigating vehicle-vehicle and vehicle–pedestrian crash severity at street intersections with the latent class parameterized correlation bivariate generalized ordered probit

Special issue “RSS2022 Conference: Advanced Road Safety Analyses”

Modeling and Analysis of Microscopic Risk Avoidance Behavior of Homogeneous Driver Groups Under Risk Scenarios

Contact Info

Product

Resources

About