Comparison of Simulation-Based and Model-Based Calibrations of Traffic-Flow Microsimulation Models

Parameter calibration of traffic microsimulation models usually takes the form of a simulation-based optimization problem, that is, an optimization in which every objective function evaluation calls for a simulation. It is recognized that such a problem is computationally intractable. Running time grows exponentially both in the number of parameters and in the digits accuracy. In addition, considerable computing time is required by each objective function evaluation. This means that only heuristic techniques can be applied. Accordingly, results of the application of the OptQuest/Multistart algorithm to the calibration of AIMSUN microsimulation model parameters on a freeway network are presented. Furthermore, it is claimed that the search for an effective solution to the calibration problem cannot be exhausted by the choice of the most efficient optimization algorithm. The use of available information concerning the phenomenon could allow calibration performance to be enhanced, for example, by reducing dimensions of the domain of feasible solutions. It is argued that this goal could be achieved by using information from the stationary counterpart of microscopic traffic-flow models that depict the aggregate variables of traffic flows as a function of drivers’ microscopic parameters. Because they have a closed analytical formulation, they are well suited for faster calibrations. Results show that values of parameters from stationary model-based calibrations are not far from the optimal ones. Thus the integration of the two approaches cannot be excluded but is worth investigating.