Dynamic traffic assignment (DTA) models are used in many transportation planning and traffic management scenario analyses today. The aim of the DTA model is to reproduce the pattern of vehicular movements. DTA models require inputs in terms of demand and capacity of the road network and are very challenging to calibrate for large urban networks. In this paper, a new network-wide calibration method for link capacities in urban networks is proposed. The method takes link flow observations for a subset of the links in the network to estimate the link capacities. The proposed method relies on partial least squares (PLS) regression and is demonstrated to be feasible and efficient in an urban road network (Stockholm, Sweden) compared to the simultaneous perturbation stochastic approximation (SPSA) method. Performance analysis of the proposed method for different amounts of link flow observations shows that it performs favorably for the cases in which only a small percentage of link flow observations is given.

Origin-Destination (OD) matrices are essential inputs to both traffic planning and management. To enable traffic management decisions, the OD matrix needs to be estimated in the order of minutes, which is very challenging in many situations. However, new large-scale mobility data, such as vehicle probe data, in combination with computationally efficient estimation methods make it possible to estimate OD matrices sufficiently fast to enable traffic management decisions. In this paper, we propose a computationally efficient method that uses link count data and vehicle probe data, within a data-driven network assignment (DDNA) framework in combination with non-negative matrix factorization (NNMF), to estimate OD demand in urban networks. Historical data are used to construct a low-dimensional description of the OD estimation problem using non-negative matrix factorization. The method and the quality of the OD matrix estimated using the low-dimensional representation are evaluated on empirical data for central Stockholm, Sweden. The results demonstrate that the method provides a computationally fast technique to find an OD matrix that provides link flow estimates close to the link flow observations for both training and test sets.