Assessing the safety effect of red-light camera deactivation: a geographically weighted negative binomial regression approach

Abstract

Municipalities across the country have debated the safety effect of automatic red-light cameras (RLC) and their political and financial implications. Most empirical studies have used the Empirical Bayesian (EB) approach to assess the safe effects to facilitate policy debates. While popular, the EB method has several limitations in data requirement, reference site selection, and control of confounding factors. Moreover, empirical studies of the RLC deactivation effects are limited. This study fills these gaps using the Moran’s I statistic and the Geographically Weighted Negative Binomial Regression (GWNBR) approach for data in the City of Arlington, Texas. The results indicate that the total, injury, and angle crashes in Arlington are on the rise over the study period and that crashes are higher at RLC deactivation intersections than those at other intersections. The direct safety effect of removing RLCs is statistically significant. The spillover effect is observed but statistically insignificant. Speed limit plays an important role in road safety. The findings have significant implications for safety research and practices.