Wavelet–k Nearest Neighbor Vehicle Classification Approach with Inductive Loop Signatures

Abstract

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.

A high-definition traffic performance monitoring system with the Inductive Loop Detector signature technology

“…RTVC-2 Performance Evaluation [14] TABLE I provides the RTVC-2 classification results by FHWA vehicle classification scheme. It was found that classification rates were lower for those classes with smaller sample size in the signature template library.…”

“…These measures can be further analyzed by vehicle type or by lane to yield even more detailed travel information if required. Furthermore, it is also shown that ILD signature technology possesses the ability to distinguish between passenger and commercial vehicle classes in the FHWA (Federal Highway Administration) vehicle classification scheme [14]- [18], [23]. …”

“…The datasets for performance evaluation included a dataset collected from the northbound I-405 corridor in the City of Irvine, California and a dataset collected from the San Onofre truck weigh and inspection facility in San Diego, California [5], [14].…”

“…Over the decades, the major research efforts for the implementation of the ILD signature technology are the development of vehicle reidentification algorithms [5]- [13] and classification algorithms [14]- [18]. The ILD signature technology has been further implemented in the real-world and its performance has been demonstrated through academic research [19], [20].…”

SimSiVIDS: Modelling of an Inductive Sensor for Traffic Applications

“…The Inductive Loop Detectors (ILDs) are sensors widely used in traffic management systems (see works [1], [2], [3], [4]). For such purposes there exist different implementations of ILDs.…”