Injury severity analysis in right-turn lanes at signalised intersections

摘要

This study investigated the level of injury severity in crashes in right-turn lanes at signalised intersections. It used a dataset of 1900 injuries occurring at 275 signalised intersections in the Las Vegas, NV, USA area in the period 2003 to 2005. An advanced random-parameter binary logit model was used to determine the factors that significantly influence injury severity - categorised as either property damage only or severe injury - in right-turn lanes. A comparison of this model with the traditional fixed-parameter model was made to account for the unobserved heterogeneity. It was comparable to the two-level binary logistic model, which accounts for cross-group heterogeneity. The analysis showed that the following factors lead to a significantly higher likelihood of severe injury: rear-end crashes, the involvement of a vehicle going through the intersection, stopped and parked vehicles on main and minor streets, length of corner clearance, number of through lanes on minor streets and intersection angle.