A preliminary investigation of the effectiveness of high visibility enforcement programs using naturalistic driving study data: A grouped random parameters approach. (March 2019)

• Record Type: Journal Article
• Authors: Pantangi, Sarvani Sonduru; Fountas, Grigorios; Sarwar, Md Tawfiq; Anastasopoulos, Panagiotis Ch.; Blatt, Alan; Majka, Kevin; Pierowicz, John; Mohan, Satish B.
• Is Part Of: Analytic methods in accident research. Volume 21(2019)
• Page Start: 1
• Page End: 12
• Publication Date: 2019-03
• Subjects: High-visibility enforcement -- Speeding -- Tailgating -- Aggressive driving behavior -- Grouped random parameters; Accidents -- Research -- Methodology -- Periodicals; Accidents -- Prevention -- Periodicals; 363.100721
• DOI: 10.1016/j.amar.2018.10.003
• Languages: English
• ISSNs: 2213-6657

Abstract

Highlights: The effectiveness of High Visibility Enforcement (HVE) programs is investigated. Novel metrics for speeding and tailgating are developed and analyzed. Grouped random parameters linear regression models are estimated for both metrics. Likelihoods of speeding and tailgating occurrences are simultaneously investigated. HVE programs have mixed effects on the extent and the likelihood of these metrics. Abstract: This paper seeks to assess the effectiveness of high-visibility enforcement (HVE) programs in terms of reducing aggressive driving behavior. Using Strategic Highway Research Program 2 (SHRP2) Naturalistic driving study (NDS) data, behavioral reactions of drivers before, during, and after the conduct of high-visibility enforcement programs are analyzed, in order to identify the potential effect of high-visibility enforcement in driving behavior. In this context, two fundamental aspects of aggressive driving behavior (speeding and tailgating) are employed and analyzed. To simultaneously explore the intensity and the duration of these behavioral patterns, novel metrics are defined and used in the analysis. To investigate the effect of high-visibility enforcement programs, and at the same time, to control for the effect of driver-, trip-, vehicle-, and weather-specific characteristics on the extent of speeding and tailgating, univariate grouped random parameters linear regression models are estimated. In addition, likelihoods of speeding and tailgating occurrences are analyzed simultaneously, within a grouped random parameters bivariate probit modeling framework. The results of this preliminary analysis show that even though the implementation of the high-visibility enforcement has mixed effects on the extent and the likelihood of the driving behavior metrics, it demonstrates a promising potential in modifying driving behavior.