Time Dependent Origin-destination Estimation from Traffic Count without Prior Information

Abstract

Existing research works on time-dependent origin-destination (O-D) estimation focus on the surveillance data usually assume the prior information of the O-D matrix (or transition matrix) is known (or at least partially known). In this paper, we relax such assumption by combining Gibbs sampler and Kalman filter in a state space model. A solution algorithm with parallel chain convergence control is proposed and implemented. To enhance its efficiency, a parallel structure is suggested with efficiency and speedup demonstrated using PC-cluster. Two numerical examples (one for Taipei Mass Rapid Transit network and the other for Taiwan Area National Freeway network) are included to show the proposed model could be effective of time-dependent origin-destination estimation.