An Approach to Sparse, Fine-Grained OD Estimation

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 using machine learning. We propose to automatically uncover a set of fine-grained traffic analysis zones based on observed link flows. We then employ appropriate 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 uncovers a set of detailed zones, and a corresponding OD matrix that accurately predicts observed link flows.