Traffic Congestion Pattern Classification Using Multiclass Active Shape Models

Abstract

Identifying and classifying traffic and congestion patterns are essential parts of modern traffic management underpinned by the emerging intelligent transport systems. This paper explores the potential of using a combination of image processing methods to identify and classify regions of congestion within spatiotemporal traffic (speed, flow) contour maps. The underlying idea is to use these regions as (archetype) shapes that in many combinations can make up a wide variety of larger-scale traffic patterns. In this paper, use of a so-called statistical shape model is proposed as a low-dimensional representation of the archetype shape, and an active shape model algorithm coupled with linear classification is developed to classify the patterns of interest. Application of the proposed method is demonstrated with a preliminary set of speed contour maps reconstructed from loop detector data in the Netherlands. The results show that the extended active shape model can be used as a multiclass classifier. In particular, 70% of the traffic patterns in the test data were correctly classified with use of only two archetype shapes and simple logistic classifiers. The results point to the importance of use of expert knowledge by means of (a priori) manual classification of the training examples. This work opens many research directions, including semiautomated searches through traffic databases, automatic detection, and classification of new traffic patterns.