01697921

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Tailgating is a common aggressive driving behavior that has been identified as one of the leading causes for rear-end crashes. Previous studies have been conducted to explore the behavior characteristics of the tailgating driver and find solutions to decrease the amount or prevalence of tailgating. This paper tries to fill the research gap by focusing on understanding tailgating scenarios and examining the leading vehicles’ reaction while being tailgated on the highway. Existing naturalistic driving data was used in this study for this purpose. Analysis of tailgating scenarios and associated factors showed that male and middle-aged drivers were most frequently involved in the tailgating events. Drivers were more likely to tailgate during sunny daytime than during other driving time. Four reaction types from leading vehicle drivers were identified and more than half of the drivers chose to change lanes when being tailgated. A Random Forests algorithm was applied in this study to predict the leading vehicle’s reaction based on relevant factors. The results showed that mean time headway, duration of tailgating, and minimum time headway were three main factors which had the greatest impact on the leading vehicle driver’s reaction. Almost all leading vehicles change lanes when being tailgated for two minutes or longer. Results of this study can help to better understand behavior of drivers in designing corresponding countermeasures or assisting systems in response to tailgating behavior.

This paper was sponsored by TRB committee AND10 Standing Committee on Vehicle User Characteristics.

19-04256

English

6p

2019

Transportation Research Board 98th Annual Meeting

Digital/other

Figures; References

Algorithms; Automatic data collection systems; Automobile drivers; Decision making; Driver performance; Lane changing; Machine learning; Tailgating

Integrated Vehicle Based Safety Systems (Initiative)

Random forest algorithm

Highways; Planning and Forecasting; Safety and Human Factors; Vehicles and Equipment

Transportation Research Board Annual Meeting 2019 Paper #19-04256

TRIS, TRB, ATRI

Dec 7 2018 9:41AM