Examining unobserved factors associated with red light running in Vietnam: A latent class model analysis. Issue 1 (21st April 2022)

• Record Type: Journal Article
• Authors: Chu, Tien Dung; Miwa, Tomio; Bui, Tuan Anh; Nguyen, Quang Phuc; Vu, Quang Huy
• Is Part Of: Transportation safety and environment. Volume 4:Issue 1(2022)
• Publication Date: 2022-04-21
• Subjects: red light running (RLR) -- developing country -- latent class model (LCM) -- multiple indicator multiple cause (MIMIC) model -- latent variables (LVs) -- motorcycles (MCs) -- traffic violation; Transportation engineering -- Periodicals; Transportation -- Safety measures -- Periodicals; 629.04
• DOI: 10.1093/tse/tdac006
• Languages: English
• ISSNs: 2631-4428

Abstract

Abstract: Red-light running (RLR) is a crucial violation that causes traffic accidents and injuries. Understanding factors that affect RLR is very significant to reduce the potential of this violation. Current studies have paid considerable attention to the observable factors, but not to unobservable factors. This study aims to examine the effects of observable and unobservable factors on RLR. This study uses a latent class model (LCM) to assign individuals into two classes—red-light-respectful and red-light-disrespectful road users—by surveying 751 respondents who use private transportation modes. This study incorporates psychological determinants into the LCM to account for unobservable factors. The contribution of this study is the in-depth investigation into law-respectful and law-disrespectful behaviours and intentional and unintentional violators. Such a study has not yet been conducted in the existing literature. In addition, a comprehensive comparison of the LCM and a traditional ordered probit model was conducted. Overall, the results suggest that the LCM is superior to the model that does not consider latent classes. Our estimation results are in alignment with previous studies on RLR: males, younger drivers/riders, less educated road users and motorcyclists are more likely to run red lights. An analysis of the latent variables shows that surrounding conditions—the behaviour of other violators, the absence of traffic police, and long waiting times—increase the possibility of violations. Based on these results, we provide suggestions to policymakers and traffic engineers: the implementation of enforcement cameras and penalties for violators are critical countermeasures to minimize the potential of RLR.