Dynamic estimation of passenger origin-destination matrix for congested transit networks

This paper investigates the problem of estimation of time-dependent passenger origin-destination (O-D) matrices in congested transit networks where real-time updated passenger counts and prior O-D matrices are available. A bi-level programming model is proposed for the dynamic estimation of passenger O-D matrix. The upper level minimizes the sum of error measurements in dynamic passenger counts and time-dependent O-D matrices, and the lower level is a new schedule-based dynamic transit assignment model that can determine simultaneously the dynamic average travel costs and route choices of passengers in congested transit networks. The lower-level problem can be formulated as a variational inequality problem. A heuristic solution algorithm is adapted for solving the proposed bi-level programming model. Finally, a numerical example is used to illustrate the applications of the proposed model and solution algorithm.