Red Light Running in Trinidad

Red Light Running (RLR) has been associated with road traffic collisions in many jurisdictions across the world. The study aimed at evaluating the factors related to red light running (RLR) in Trinidad and Tobago that could be incorporated into policies that would aim to reduce this tendency and its related costs. Using data collected from the Traffic Unit of Trinidad and Tobago and three major intersections in the country, analysis was done on the impact of time of the day, age of the driver, ethnicity, gender, type of the vehicle, location and day of the week which are related to RLR. The results showed that the highest number of RLR occurs during rush hours in the morning and also in the evenings. Also, Indo-Trinidadian and male drivers display a higher tendency towards RLR. It was also established that some junctions have higher RLR rates than others and that Fridays have more RLR cases than all the other days of the week. These findings are very similar to those made in other countries such as the USA. The study has resulted in specific findings that can be used to improve traffic safety policies in the country.

Safety impacts of red light running photo enforcement at urban signalized intersections

Red light running at signalized intersections is a major safety concern in the United States. Statistics show that approximately 45 percent of crashes at intersections caused by red light running re- sult in severe injuries and fatalities, while only approximately 30 percent of all other types of intersec- tion crashes cause injuries or fatalities. Over the past decade, many US cities and counties have de- ployed red light running photo enforcement systems for signalized intersections within their jurisdictions to potentially reduce red light running related crashes. This study proposes an empirical Bayesian （ EB ） before-after analysis method that computes a weighed sum of crashes observed in the field and crashes predicted by safety performance functions （SPFs） to mitigate regression-to-mean biases for analyzing crash reduction effects of red light running enforcement. The analysis explicitly considers red light run- ning related crash types, including head-on, rear-end, angle, tuming, sideswipe in the same direction, and sideswipe in the opposite direction; and crash severity levels classified as fatal, injury, and proper- ty damage only （PDO）. A computational study is conducted to examine the effectiveness of the Chica- go program with red light running photo enforcement systems deployed for nearly two hundred signal- ized intersections. It is revealed that the use of red light running photo enforcement on the whole is pos- itive, as demonstrated by reductions in all types of fatal crashes by 4-48 percent, and injury-related an- gle crashes by 1 percent. However, it slightly raises PDO-related angle crashes and moderately increa- ses injury and PDO related rear-end crashes. The safety effectiveness of red light running photo en- forcement is sensitive to intersection location.

Examining unobserved factors associated with red light running in Vietnam:A latent class model analysis

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.