A Procedure of Global Optimization and its Application to Estimate Parameters in Interaction Spatial Models

摘要

This work concerns the calibration of trip distribution models issued from Transportation Planning. The optimal estimators are the global minima of a non-convex function. A mathematical result establishes that the solution is the limit of a sequence of means of convenient random variables. A population-based global optimization involving this representation and random perturbations of a local descent method is applied. Experiments are performed on classical test functions. In the application to the models of trip distribution, 4 sets of data, including real data are used. The results show that the use of the representation of the solution leads to a significant improvement and increases the robustness.