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 as well as driver demographics influence the right-angle crash vulnerability of motorcycles at intersections. The problem is modeled using a mixed logit model with a binary choice category formulation to differentiate how an at-fault vehicle collides with a not-at-faultmotorcycle in comparison with other vehicle types. The mixed logit formulation allows for randomness in the parameters and hence takes into accountthe 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. Moreover, the presence of several random parameters in the model estimation also ensures the credibility of this model. Night time riding shows a positive association with the vulnerability of motorcyclists. Motorcyclists are particularly vulnerable on single lane roads, on curb lane and median lane of multi-lane roads, and on one-way and two-way traffic type relative to divided-highway. Drivers motive to deliberate red light running as well as theircarelessness to motorcyclists especially during making turns at intersections increase the vulnerability of motorcyclists. The drivers' risky behaviors seem to be restrained with the presence of a passenger and this has also decreased the crash potential with motorcyclists. The presence of red light cameras significantly decreases right-angle crash vulnerabilities ofmotorcyclists. The findings of this study would be helpful in developing more targeted countermeasures for traffic enforcement, driver/rider training and/or education, safety awareness programs to reduce the vulnerabilityof motorcyclists.
Abstract