Multi-Class Vocation Identification for Heavy Duty Vehicles

Abstract

Understanding the operating profile of different heavy-duty vehicles is needed by parts manufacturers for improved configuration and better future design of the parts. This study investigates the use of a tournament classification approach for both vocation and fleet identi- fication. The proposed approach is implemented using four different classification techniques, namely, K-Means, Expectation Maximization, Particle Swarm Optimization, and Support Vector Machines. Vocations classifiers are developed and tested for six different vocations ranging from coach buses to rail inspection vehicles. Operational field data are obtained from a number of vehicles for each vocation and aggregated over a pre-set distance that varies according to the data collection rate. In addition, fleet classifiers are implemented for five fleets from the coach bus vocation using a similar approach. The results indicate that both vocation and fleet identification are possible with a high level of accuracy. The macro average precision and recall of the SVM vocation classifier are approximately 85%. This result was achieved despite the fact that each vocation consisted of multiple fleets. The macro average precision and recall of the coach bus fleet classifier are approximately 77% even though some fleets had similar operating profiles. These results suggest that the proposed classifier can help support vocation and fleet identification in practice.