Effects of Highway Landscapes on Drivers’ Eye Movement Behavior and Emergency Reaction Time: A Driving Simulator Study

This study aims at investigating the effects of highway landscapes and alignments on drivers’ eye movement behavior and emergency reaction time, based on a driving simulator experiment. In this study, four simulation scenarios are evaluated including open space, semiopen space, semiclosed space, and...

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Bibliographic Details
Published in:Journal of advanced transportation 2019-01, Vol.2019 (2019), p.1-9
Main Authors: Xiong, Jian, Chen, Yao, Li, Mingyuan, Guo, Fengxiang, Lee, Jaeyoung
Format: Article
Language:English
Subjects:
Alignment
Behavior
Computer simulation
Data analysis
Driver behavior
Drivers
Emergencies
Experiments
Eye movements
Freeways
Highway design
Highways
Laboratories
Reaction time
Reaction time task
Regression analysis
Regression models
Roads & highways
Simulation
Statistical analysis
Statistical tests
Studies
Traffic
Transportation
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Summary:This study aims at investigating the effects of highway landscapes and alignments on drivers’ eye movement behavior and emergency reaction time, based on a driving simulator experiment. In this study, four simulation scenarios are evaluated including open space, semiopen space, semiclosed space, and enclosed space landscapes on highways in Yunnan Province, China. Twenty-four experienced drivers participated in a 6-kilometer driving experiment in each landscape scenario. Each subject was required to drive at 80 km/h in the scenarios and the driving behavior data were collected. Three different data analysis methods were employed: (1) descriptive analysis of the characteristics of drivers’ visual fixation area; (2) statistical tests of emergency reaction time with drivers’ demographic characteristics, highway landscapes, and alignments; and (3) multiple linear regression analysis of emergency reaction time, highway landscapes, and alignments. The results show that emergency reaction time is significantly influenced by highway landscapes and alignments, and the multiple linear regression model built in this experiment could accurately predict drivers’ emergency reaction time in different highway landscapes and alignments.
ISSN:0197-6729
2042-3195
DOI:10.1155/2019/9897831