A statistical procedure for estimating a mean origin-destination matrix from a partial registration plate survey

This paper addresses the problem of matching partial registration plate data collected at a number of origins and destinations in a traffic system, the aim being to reconstruct the pattern of vehicle movements. The matching algorithm proposed here embraces two of the most appealing features of the methods used in most commercially available packages-the use of passage times to improve the accuracy of the matching process and the ability to handle data at any number of origins and destinations-but does so within a statistical framework. The new method is seen to have a number of significant advantages ove existing approaches. A model is firstly proposed for the two observation point case and the algorithm derived, in which, alternately, the most probable complete matching consistent with the observed data and the maximum likelihood estimates of the parameters of the model are determined. An efficient solution algorithm is described. The approach is then generalised to handle data collected at any number of origins and destinations. Finally, the performance of the proposed method is studied in applications to simulated data sets, where the solution yielded by the algorithm is compared with the (known) true matching of the data.