Fine-grained OD estimation with automated zoning and sparsity regularisation |
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| Institution: | 1. National ICT Australia and the Australian National University, Canberra, ACT, Australia;2. National ICT Australia and the University of New South Wales, Sydney, NSW, Australia;1. German University of Technology in Oman, PO Box 1816, Athaibah PC130, Muscat, Oman;2. Department of General Ecology, Kazan Federal University, Russia;3. Department of Soils, Water and Agricultural Engineering, Sultan Qaboos University, Oman;1. Department of Mathematics and its Applications, Central European University, Nador ut. 9, Budapest, 1051, Hungary;2. Laboratory of Graph Theory, Sobolev Institute of Mathematics, Acad. Koptyug av. 4, Novosibirsk, 630090, Russia;3. Renyi Institute of Mathematics, Budapest, Reáltanoda ut. 13-15, 1053, Hungary;4. Technical University of Budapest, Budapest, Budafoki ut. 8, 1111, Hungary;5. Center of Network Science, Central European University, Nador ut. 9, Budapest, 1051, Hungary;1. College of Metropolitan Transportation, Beijing University of Technology, Beijing, China;2. Faculty of Applied Mathematics and Control Processes, St. Petersburg State University, St. Petersburg, Russia;1. Griffith School of Engineering, Griffith University, Gold Coast, QLD 4222, Australia;2. Strome College of Business, Old Dominion University, Norfolk, VA 23529, USA |
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| Abstract: | Given a road network, a fundamental object of interest is the matrix of origin destination (OD) flows. Estimation of this matrix involves at least three sub-problems: (i) determining a suitable set of traffic analysis zones, (ii) the formulation of an optimisation problem to determine the OD matrix, and (iii) a means of evaluating a candidate estimate of the OD matrix. This paper describes a means of addressing each of these concerns. We propose to automatically uncover a suitable set of traffic analysis zones based on observed link flows. We then employ regularisation to encourage the estimation of a sparse OD matrix. We finally propose to evaluate a candidate OD matrix based on its predictive power on held out link flows. Analysis of our approach on a real-world transport network reveals that it discovers automated zones that accurately capture regions of interest in the network, and a corresponding OD matrix that accurately predicts observed link flows. |
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| Keywords: | OD estimation Traffic analysis zones Sparsity |
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