Granular Analysis of Traffic Data for Turning Movements Estimation

Granular Analysis of Traffic Data for Turning Movements Estimation

Andrzej Bargiela (The Nottingham Trent University, UK), Iisakki Kosonen (Helsinki University of Technology, Finland), Matti Pursula (Helsinki University of Technology, Finland) and Evtim Peytchev (The Nottingham Trent University, UK)
Copyright: © 2006 |Pages: 15
DOI: 10.4018/jeis.2006040102
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The paper discusses the principles and the algorithm of granular analysis of data in a specific context of urban traffic monitoring and control (EIS). The proposed granular information processing enables extraction of information on the pattern of journeys from the detailed traffic counts. This facilitates progression from the local optimisation of traffic on individual crossroads to the more holistic optimisation of traffic in a road network. The proposed EIS makes use of readily available stop-line queue data, which is used for adaptive tuning of traffic signals, and adds a data processing layer referred to as granular analysis. It is argued that granular analysis is preferred to statistical data processing since it does not require any assumptions about statistical characterisation of traffic. The granulation algorithm has two distinctive features: (1) the information granules are formed by means of hierarchical optimisation of information density, and (2) the granules are created as hyperboxes thus being readily interpretable in the pattern space. The granular estimates of turning movements are calibrated using an HUTSIM micro-simulator.

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