Estimation of Traffic Demand Corresponding to Observed Link Traffic Volume in Microscopic Simulation

Abstract

Traffic simulation is utilized to solve traffic-related problems. Microscopic simulations can describe individual vehicles and thus reproduce detailed vehicle behavior. To use a simulator, traffic demand should be estimated in the form of an origin-destination (OD) matrix. The simulator and OD estimation models must be consistent. In addition, microscopic models are sensitive to congestion, and can thus easily produce unexpected congestion. Here, we propose a simulator-embedded OD estimation method that uses congestion sensing. We minimize the residual between the observed and simulated link traffic volumes with some constraints regarding congestion. If a link is judged to be congested, we use resistance in a constraint in the optimization problem, which is determined by the number of the stuck vehicles at each link. Use of the resistance prevents excessively large traffic demand for that link. This congestion sensing mitigates unrealistic congestion in the estimated traffic demand.