Abstract
This study focuses on updating time-varying demand matrices by using real observation counts from advanced traffic management surveillance systems. A machine-learning technique using advanced evolutionary algorithms (EAs) is developed instead of the more conventional approaches in the literature. This EA-based demand estimation framework is implemented into a model called the Dynamic Origin–Destination (O-D) Estimator (DynODE). The potential of EAs in the dynamic O-D estimation problem lies in their powerful global search and optimization capabilities. DynODE is integrated with an existing dynamic traffic assignment platform (e.g., DYNASMART-P). The EA-based methods in this study are further augmented with EA parallelization to improve the quality and efficiency of the solution. DynODE mainly addresses offline O-D estimation problems. However, online O-D estimation can be achieved with the parallel version of DynODE with sufficient multiprocessing and parallel computing. The developed approach is rigorously evaluated on a medium-sized real network to assess the effects of various parallel structures. For all experiments, savings in computation resources as well as enhancement in the quality of solution were realized.
Get full access to this article
View all access options for this article.