Time-dependent origin-destination estimation: Genetic algorithm-based optimization with updated assignment matrix

Abstract

An estimation of origin-destination (OD) demand matrix is one of key elements to ensure the success in traffic modeling analysis. Even with widely deployed traffic sensors and advanced computational technologies, an estimation of time-dependent OD matrices is still a key barrier for the implementation of dynamic traffic assignment as well as simulation-based traffic modeling analysis. This paper proposes an improvement of existing time-dependent OD estimation method by updating assignment matrix at each step of OD estimation process and quantifies benefits and costs of doing so. The results from a case study with Florian network showed that the estimated OD flows from the proposed GA-based method with updated assignment matrix reduced the sum of square errors by 40% when compared with the OD flows from the DynaMIT OD estimation method with fixed assignment matrix, one of the most commonly used OD estimation methods. However, the proposed method would require significantly higher computational time than the traditional DyanMIT OD estimation method.