A Multivariate Analysis Framework for Vehicle Detection From Loop Data Under Heterogeneous and Less Lane Disciplined Traffic

Abstract

Accurate vehicle detection in real-time is a challenging problem for engineers and researchers working in the field of transportation engineering all over the world. To detect the vehicle presence in the lane-based traffic, one of the widely used traffic detectors is inductive loop detectors (ILD). For less lane disciplined and heterogeneous traffic, researchers have suggested another traffic detector known as a multiple inductive loop detector (MILD) system. In order to extract the vehicle count from MILD data, it is required to detect vehicle presence and segment the signature of different vehicles. This work is focused on the automated processing of MILD signals to get total vehicle count information in real-time under heterogeneous and less-lane disciplined traffic conditions. This study proposes a multivariate data analysis framework for the detection and segmentation of vehicle signature from the acquired data, without significant manual intervention. The major challenge in this process is the coupling of the multi-dimensional loop data, due to cross-talk across the loops. To address this, principal component analysis (PCA) is used with the additional benefit of dimensionality reduction. Though PCA is a well-known method, its application to the current problem is not trivial and calls for the tailoring of the method. Here, a new PC selection strategy suitable for data under consideration is proposed, as the traditional approach does not fit this application and tends to be low accuracy. Subsequently, the principal components are processed using a threshold-based method, which uses the mean absolute deviation measure, to detect the vehicle presence. The results show that the developed algorithm with the proposed strategy for PC selection achieved an average vehicle count accuracy of 90.38 % whereas with the traditional approach the accuracy is 24.31 %.