Right-angle crash vulnerability of motorcycles at signalized intersections: Mixed logit analysis

Abstract

Motorcycles are particularly vulnerable in right-angle crashes at signalized intersections. The objective of this study is to explore how variations in roadway characteristics, environmental factors, traffic factors, maneuver types, human factors, and driver demographics influence the rightangle crash vulnerability of motorcycles at intersections. The problem is modeled by using a mixed logit model with a binary choice category formulation to differentiate how an at-fault vehicle collides with a notat-fault motorcycle in comparison with other collision types. The mixed logit formulation allows randomness in the parameters and hence takes into account the underlying heterogeneities potentially inherent in driver behavior and other unobserved variables. A likelihood ratio test reveals that the mixed logit model is indeed better than the standard logit model. Nighttime riding shows a positive association with the vulnerability of motorcyclists. Moreover, motorcyclists are particularly vulnerable on single-lane roads, on the curb and median lanes of multilane roads, and on one-way and two-way roads relative to divided highways. Drivers who deliberately run red lights and those who are careless toward motorcyclists, especially when turning at intersections, increase the vulnerability of motorcyclists. Drivers appear more restrained when there is a passenger onboard, and this factor has decreased the crash potential for motorcyclists. The presence of red light cameras also significantly decreases right-angle crash vulnerabilities of motorcyclists. The findings of this study would be helpful in developing more targeted countermeasures for traffic enforcement, driver or rider training or education, and safety awareness programs to reduce the vulnerability of motorcyclists.