Modeling drivers' reaction when being tailgated: A Random Forests Method

Yueru Xu; Shan Bao; Anuj K Pradhan

doi:10.1016/j.jsr.2021.05.004

Modeling drivers' reaction when being tailgated: A Random Forests Method

J Safety Res. 2021 Sep:78:28-35. doi: 10.1016/j.jsr.2021.05.004. Epub 2021 May 25.

Authors

Yueru Xu¹, Shan Bao², Anuj K Pradhan³

Affiliations

¹ Intelligent Transportation System Research Center, Southeast University, Nanjing 211189, China; Mechanical and Industrial Engineering Department, University of Massachusetts Amherst, Amherst, MA 01003, USA.
² Industrial and Manufacturing Systems Engineering Department, University of Michigan-Dearborn, 4901 Evergreen Rd, Dearborn, MI 48128, USA; University of Michigan Transportation Research Institute, 2901 Baxter Rd, Ann Arbor 48109, MI, USA. Electronic address: shanbao@umich.edu.
³ Mechanical and Industrial Engineering Department, University of Massachusetts Amherst, Amherst, MA 01003, USA.

PMID: 34399925
DOI: 10.1016/j.jsr.2021.05.004

Abstract

Background: Tailgating is a common aggressive driving behavior that has been identified as one of the leading causes of rear-end crashes. Previous studies have explored the behavior of tailgating drivers and have reported effective solutions to decrease the amount or prevalence of tailgating. This paper tries to fill the research gap by focusing on understanding highway tailgating scenarios and examining the leading vehicles' reaction using existing naturalistic driving data.

Method: A total of 1,255 tailgating events were identified by using the one-second time headway threshold criterion. Four types of reactions from the leading vehicles were identified, including changing lanes, slowing down, speeding up, and making no response. A Random Forests algorithm was employed in this study to predict the leading vehicle's reaction based on corresponding factors including driver, vehicle, and environmental variables.

Results: The analysis of the tailgating scenarios and associated factors showed that male drivers were more frequently involved in tailgating events than female drivers and that tailgating was more prevalent under sunny weather and in daytime conditions. Changing lanes was the most prevalent reaction from the leading vehicle during tailgating, which accounted for more than half of the total events. The results of Random Forests 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 drivers' reaction. It was found that in 95% of the events, leading vehicles would change lanes when being tailgated for two minutes or longer. Practical Applications: Results of this study can help to better understand the behavior and decision making of drivers. This understanding can be used in designing countermeasures or assistance systems to reduce tailgating behavior and related negative safety consequences.

Keywords: Driving behavior; Naturalistic driving data; Random Forests; Road safety; Tailgating.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Accidents, Traffic*
Aggression
Automobile Driving*
Female
Humans
Male
Weather