Highway Traffic Congestion Patterns: Feature extraction and Pattern retrieval

Traffic congestion occurs daily, which can have negative effects on not only the quality of mobility, but also other important aspects of life like economic growth, health and environment. Both understanding and efficiently managing traffic are therefore crucially important tasks. Vast amounts of data are collected daily to gain insights into the dynamics of traffic. However, these data are typically stored in the form of raw measurements, that might hamper their potential benefits to both researchers and practitioners. A more informative and compact way to store traffic data is in the form of spatio-temporal maps, which have been shown to have advantage in intuitively observing traffic states. However, collecting, managing and retrieving such 2D patterns of congested traffic on large networks are challenging tasks. Accordingly, this dissertation is dedicated to developing methodologies and tools to advance the utilisation of traffic data, in particular, congestion patterns. A conceptual framework for an intelligent search engine for congestion patterns (socalled CoSI) is designed. It covers the entire requirements necessary to develop such a system, ranging from processing raw data to searching through the resulting database of congestion patterns. Overall, the framework consists of two parts: database construction and search application (or so-called pattern retrieval). Their designs and relations are comprehensively presented in this research. The database construction is responsible for preparing a database of patterns of congested traffic which is carefully designed for the conveniences of a search application. Its conceptual design consists of three layers (or phases): pattern collection, feature extraction and pattern annotation. Regarding the search application, several possibilities for retrieving patterns are identified in association with the aforementioned steps of constructing the underlying database.