Do High Visibility Enforcement programs affect aggressive driving behavior? An empirical analysis using Naturalistic Driving Study data. (April 2020)

• Record Type: Journal Article
• Authors: Pantangi, Sarvani Sonduru; Fountas, Grigorios; Anastasopoulos, Panagiotis Ch.; Pierowicz, John; Majka, Kevin; Blatt, Alan
• Is Part Of: Accident analysis and prevention. Volume 138(2020)
• Publication Date: 2020-04
• Subjects: High-visibility enforcement -- Speeding -- Tailgating -- Unsafe lane changes -- Aggressive Driving Behavior -- Grouped random parameters; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2019.105361
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: Developed scaled metrics to capture intensity and duration of aggressive driving. Several surrogate safety measures were investigated. Unobserved heterogeneity was accounted for in all statistical models. Results show that high visibility enforcement has the potential to improve safety. Abstract: This paper investigates the effect of High Visibility Enforcement (HVE) programs on different types of aggressive driving behavior, namely, speeding, tailgating, unsafe lane changes and 'other' aggressive driving behavior types (occurrence of not-yielding right-of-way and red light or stop signs violations). For this purpose, the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) data are used, which include forward-facing videos and time series information with regard to trips conducted at or near the locations of HVE implementation. To capture the intensity and duration of speeding and tailgating, scaled metrics are developed. These metrics can capture varying levels of aggressive driving behavior enabling, thus, a direct comparison of the various behavioral aspects over time and among different drivers. To identify the effect of HVE and other trip, driver, vehicle or environmental factors on speeding and tailgating, while accounting for possible interrelationship among the behavior-specific scaled metrics, Seeming Unrelated Regression Equation (SURE) models were developed. To analyze the likelihood of occurrence of unsafe lane changes and 'other' aggressive driving behavior types, a grouped random parameters ordered probit model with heterogeneity in means and a correlated grouped random parameters binary logit model were estimated, respectively. The results showed that drivers' awareness of HVE implementation has the potential to decrease aggressive driving behavior patterns, especially unsafe lane changes and 'other' aggressive driving behaviors.