Sensitivity Analysis of an Evolutionary-Based Time-Dependent Origin/Destination Estimation Framework

This paper presents sensitivity analysis results for a distributed evolutionary implementation of the estimation of time-dependent origin–destination (TDOD) matrices. The system uses a noniterative origin–destination (OD) estimation process, which attempts to minimize the discrepancy between observed link flow counts and assigned counts. At the same time, the system anchors the search process to the vicinity of an a priori OD matrix to maintain travel patterns and OD structure. The sensitivity analysis examines various factors that are expected to affect the performance of the evolutionary-based TDOD estimation method. The factors examined are congestion levels, network size, size of the search space, and degree of precision of the a priori matrix. Simulation results for the waterfront network in Toronto, ON, Canada, show that the algorithm is robust in terms of replicating observed vehicle counts and the closeness to the real demand. The use of distributed evolutionary algorithm is also shown to provide good results for a large network and within fast computing speeds. However, the quality of the estimated OD relative to the true OD is shown to deteriorate if a totally random a priori matrix is used as a starting point, which has no structural resemblance to the prevailing OD patterns in the network. In addition, the quality of the estimated OD matrix was found to deteriorate as congestion levels and the network size increased. It is notable that, in all cases, the estimated OD matrix resulted in better matching flows.