A dynamic estimation of passenger OD matrices based on space-state models

Abstract

Dynamic OD passenger matrices must be taken into account when designing robust public transport systems. The adjustment of these matrices is examined using passenger counts provided by a space-state model formulation in a demand-variable peak-period. The Kalman filter approach also incorporates ICT data, based on the detection of the electronic signature of on-board devices, thus providing a rich source of data that can be used in space-state models to simplify its formulation. It reduces the dimension of the state vector and allows a linear mapping between state variables and measurements.