Estimating the Safety Benefits of Red Light Cameras at Signalized Intersections in Urban Areas Case Study: The City of Virginia Beach

The Highway Safety Manual [HSM, 2010] recommends safety evaluations be performed beforeimplementing any roadway treatment to predict the expected safety consequences. Safety consequences canbe measured using crash prediction models, Crash Modification Factor (CMFs), or both. This paperdevelops a CMF to show the expected impact of red-light cameras (RLCs) on safety at signalizedintersections. A CMF is a multiplicative factor used to compute the expected number of crashes afterimplementing a given countermeasure at a specific roadway site. RLCs are intended to improve driver’salertness to avoid causing accidents. This paper analyzes accident data reported at thirteen signalizedintersections in Virginia Beach in 2008 before the RLCs were installed and 2010 after the RLCs wereinstalled. Safety performance functions (SPFs) and the empirical Bayes (EB) before-after methodologies areused to develop a CMF for this countermeasure. The result shows an overall CMF of 0.846, which is a15.4% safety improvement. This result is not absolute; however, but sets a starting point for furtherinvestigations and potential inclusion in the future editions of the HSM.

Estimating the Safety Benefits of Red Light Cameras at Signalized Intersections in Urban Areas Case Study: The City of Virginia Beach Keywords:

Safety , crash modification factors , red-light cameras , signalized intersections , empirical Bayes