Refined Vehicle Classification for BWIM Traffic Data and the Effects on Traffic Load Modelling

Applications of BWIM typically provide information on the type of detected vehicles adopting a simple axle configuration–based classification scheme. Such an approach potentially merges different truck types with fundamentally diverging properties into a single group of vehicles represented by the same statistical parameter models for numeric traffic simulations. Focusing on two prominent vehicle types, this work proposes a more detailed classification scheme for BWIM data, exploiting information on vehicles' axle spacings and axle sequence lengths. The objective is distinguishing different truck types exhibiting noticeably diverging statistical behavior regarding relevant vehicle parameters but falling within the same axle configuration–based vehicle class. For evaluating the effects on traffic load modeling, the resulting load effects for a set of representative influence lines are calculated and compared applying the different classification schemes.