A before-after full bayes multivariate intervention model to estimate the safety effectiveness of red light cameras.

Objective: Intersection-related crashes account for approximately 40% of all crashes and tend to be more severe. Red-light running (RLR) crashes are most severe as almost half of these crashes result in injuries and fatalities. To reduce RLR crashes, agencies have been deploying red light cameras (RLCs).

Estimation of bicycle crash modification factors (CMFs) on urban facilities using zero inflated negative binomial models.

The objective of this study was to develop crash modification factors (CMFs) for bicycle crashes for different roadway segment and intersection facility types in urban areas. The study used four years (2011-2014) of crash data from Florida to quantify the safety impacts of roadway and traffic characteristics, bicycle infrastructure, and bicycle activity data on bicycle crashes. A cross-sectional analysis using Generalized Linear Model (GLM) approach with Zero Inflated Negative Binomial (ZINB) distribution was adopted to develop the relevant regression models in this study.

Data mining in road crash analysis: the context of developing countries.

The recent advancements in the field of data mining have made vast progress in extracting new information and hidden patterns from large datasets which are often overlooked by the traditional statistical approaches. These methods focus on searching for new and interesting hypothesis which were previously unobserved. Road safety researchers working with the crash data from developed world have seen encouraging success in obtaining new insight into crash mechanism through data mining.