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Model of State Spaces for Estimating the Dynamic Origin–Destination Matrix for a Public Transport Network by Applying the Kalman Filter

Abstract:

A model to estimate the passenger flow based on the dynamic origin–destination matrix applied at the South-Eastern Corridor from the terrestrial transport network in the city of Quito was the goal of this project. In the model, first: The state space was defined for the transport network, and later, was applied the Kalman Filter algorithm to get the dynamic origin–destination matrix. The state variables defined were the origin–destination pairs, the temporal variation, and the speed changes in the origin–destination pairs. Finally, the measure vector, the evolution matrix model, the process noise matrix, and the measurement error matrix were developed.