Robustness and Computational Efficency of a Kalman Filter Estimator of Time Dependent Od Matrices Exploiting Ict Traffic Measurements

Origin-Destination (OD) trip matrices, which describe the patterns of traffic behavior across the network, are the primary data input used in principal traffic models and therefore, a critical requirement in all advanced systems that are supported by Dynamic Traffic Assignment models. However, because OD matrices are not directly observable, the current practice consists of adjusting an initial or seed matrix from link flow counts which are provided by an existing layout of traffic counting stations. The availability of new traffic measurements provided by Information and Communication Technologies (ICT) applications allows more efficient algorithms, namely for the real-time estimation of OD matrices based on modified Kalman Filtering approaches exploiting the new data. The quality of the estimations depends on various factors, like the penetration of the ICT devices, the detection layout and the quality of the initial information. Concerning the feasibility of real-time applications, another key aspect is the computational performance of the proposed algorithms for urban networks of sensitive size. This paper presents the results of a set of computational experiments with a microscopic simulation of a network of the business district of Barcelona, which explore the sensitivity of the Kalman Filter estimates with respect to the values of the design factors, and its computational performance.