Experimental road safety study of the actual driver reaction to the street ads using eye tracking, multiple linear regression and decision trees methods

• Published in: Expert systems with applications Vol. 252; p. 124222
• Main Author: AlKheder, Sharaf
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.10.2024
• Subjects: ANOVA test; Decision tree estimation; Driver’s reaction; Eye tracking; Kuwait; Multiple linear regression; Road safety; Roadside advertising; Decision tree estimation; Driver’s reaction; Road safety; Multiple linear regression; Kuwait; Roadside advertising; Eye tracking; ANOVA test
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2024.124222

The article describes the results of a naturalistic driving study done in Kuwait performed by 34 participants wearing a mobile eye tracker to monitor the effect of roadside advertisements on user attention. Eye-tracking (fixations) are the main dependent variable and examined as a function of driving/roadside characteristics, particularly billboards, speed, and so forth. The results obtained were analyzed using traditional statistics (ANOVA test and multiple linear regression) and machine learning (decision tree estimation methods). From the results, it was found that road advertisements negatively affect driver attention and thus road safety. How the level of safety varies with the type and size of advertisement was also investigated. As a consequence, all of the estimates revealed that different aspects of advertising have a detrimental impact on drivers' behavior, and that the duration of fixation and the rate of acceleration before viewing an ad are impacted by advertising type and size, respectively.