Red-light cameras at intersections: Estimating preferences using a stated choice model

Abstract

Red light cameras placed at intersections have the potential to increase safety, but they are often viewed as an invasion of privacy. Preferences for these cameras were explored using a stated choice model that presents key attributes of camera placements. Stated choice models involve careful experimental design, akin to experimental control in laboratory settings. A variety of design approaches were used, settling on a composition of the choice sets people face in the survey. To illustrate the approach an internet survey was used and a convenience sample containing a high percentage of college students. The results show that while not the case independently, as the number of cameras and the fine for violators are simultaneously increased, the preferences for one particular RLC program are likely to improve. __________________

Unveiling the relevance of traffic enforcement cameras on the severity of vehicle–pedestrian collisions in an urban environment with machine learning models

“…Previous studies have investigated the effect of enforcement strategies on pedestrian safety, focusing on the impact of combining photo capturing and traffic lights with different combinations of infrastructure designs at several geographic locations (Christie, 2003;Cohn et al, 2020;Egbendewe-Mondzozo et al, 2010;Hu et al, 2011;Hu & Cicchino, 2018;Lee et al, 2014;H. Li et al, 2020;Retting et al, 1999;Walden, 2008).…”

A novel model of increasing block fine for red-light running recidivism

“…Enforcement countermeasures aim at encouraging drivers to comply with traffic laws via imposing a citation, a certain penalty point, and a possible fine. 14 Since 1 January 2013, the Provisions on the Application and Use of Motorized Vehicle Driving License issued by Ministry of Public Security of China have doubled the penalty points and fine (i.e. from 3 points and a fine of 100 RMB (Renminbi) to 6 points and a fine of 200 RMB) for RLR runners.…”