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.
In this study, a new vehicle classification algorithm was developed with inductive loop signature technology. There were two steps to the proposed algorithm. The first step was to use the Haar wavelet to transform and reconstruct inductive vehicle signatures, and the second step was to group vehicles into FHWA vehicle types through the use of the k nearest neighbor (KNN) approach with a Euclidean distance classifier. To determine the proper proportion of the wavelet to apply for reconstruction and feature extraction, transformed signatures were examined with percentages of large components of their corresponding wavelets. To implement the KNN approach, a library of vehicle signature templates for each FHWA vehicle class was composed. The proposed vehicle classification algorithm demonstrated promising classification results, with a 92.4% overall accuracy. The algorithm can be applied to the real world without the concerns about recalibration and transferability that arise with the use of signature data from single loops. Two additional vehicle classification schemes were applied for performance evaluation. For the inductive signature performance evaluation classification scheme, which aimed to facilitate emission analysis and easy interpretation, the overall accuracy was 94.1%. For the axle-based vehicle classification scheme proposed in this project, which aimed to group vehicles by use and the number of axles, the overall accuracy was 93.8%. Future research will focus on refinement of the signature template library for each FHWA vehicle type to further improve the performance of the proposed vehicle classification algorithm. The selection of the value of k for the KNN approach will be investigated also.
With continuing emphasis on transportation sustainability and fiscal stewardship, utilizing existing loop detector infrastructure to obtain more accurate, reliable, and comprehensive traffic system performance measures is desired by many transportation agencies. We found that the capability of the Inductive Loop Detector (ILD) signature technology to reidentify and classify vehicles along a section of roadway have the potential to provide better performance measures. Therefore, we proposed a high-definition traffic performance monitoring system (Traffic Monitor HD) based on the ILD signature technology and existing loop infrastructure for both freeway and arterial applications. Compared to the traditional performance measurement system, the advantages of the ILD signature technology allow Traffic Monitor HD to provide more comprehensive and accurate performance measurements, including point-based measures (i.e., vehicle counts, classification, and alerts on problematic detectors), sectionbased measures (i.e., travel time, speed, and estimates on emission), and O-D based measures (i.e., O-D matrix and trip travel time).
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