Real-Time Vehicle Classification Using Inductive Loop Signature Data

Vehicle class is an important characteristic of traffic measurement, and classification information can contribute to many important applications in various transportation fields. For instance, vehicle classification is helpful in monitoring heavy vehicle traffic for road maintenance and safety, modeling traffic flow, and obtaining performance measurements based on each vehicle class for traffic surveillance. A real-time vehicle classification model was introduced. A heuristic method combined with decision tree and K-means clustering approaches was proposed to develop the vehicle classification model. The features used in the proposed model were extracted from piecewise slope rate values, which were obtained from single-loop inductive signature data. Three vehicle classification schemes-FHWA, FHWA-I, and Real-time Traffic Performance Measurement System-and a data set obtained from square single-loop detectors were used for model development. A data set obtained from round single-loop detectors was applied to test the transferability of the proposed model. The results demonstrated that the proposed real-time vehicle classification model is not only capable of categorizing vehicle types on the basis of the FHWA scheme but also is capable of grouping vehicles into more detailed classes. The classification model can successfully classify vehicles into 15 classes using single-loop detector data without any explicit axle information. In addition, the advantages of the proposed vehicle classification model are its simplicity, its use of the current detection infrastructure, and its enhancement of the use of single-loop detectors for vehicle classification. The initial results also suggest the potential for transferability of the vehicle classification approach and are very encouraging.