Modeling Signalized-Intersection Safety with Corner Clearance

Abstract

Signalized intersections next to each other on the same arterial share some unobservable information, such as traffic flow and roadway characteristics. This study investigated the impact of access management techniques on crash counts at signalized intersections. The analysis was performed using crash data from 275 signalized intersections in southern Nevada. The panel data random-effect model was used to account for the unobserved factors for each unique arterial. It was found that the negative binomial (NB) regression models were the best in reflecting the dispersion in the crash data. Therefore, the random-effects negative binomial model (RENB) was applied to investigate the relationship between crash occurrence and access-management techniques. The results of the panel data RENB models were compared with those from the pooled NB models, which did not account for the panel data structure. Evaluation of the goodness-of-fit of the models developed indicated that the random-effect negative binomial model was the best-fit for the data at hand. The results from the panel data RENB showed that nine variables significantly affecting the safety at signalized intersections were the average length of corner clearance, traffic flow, land-use types, number of left-turn lanes for main streets, number of through lanes for main and minor streets, posted speed limit on main and minor streets, and grades of legs.