Developing a Dynamic Traffic Assignment Model for Large-Scale Networks: A Case Study in the City of Yazd, Iran

In this paper, a new dynamic traffic assignment (DTA) model is proposed for large-scale networks. Existing DTA merely describe traffic flow problems based on the travel time holographic principle, which is easy to distribute the bulk of traffic flows on the shortest path. Among the models examined in this respect, the solution method via estimating maximum correctness and expanding logarithmic correction is suggested. The given method is then validated on the Sioux Falls (SF) sample network, whose results indicate that the proposed solution method is appropriate. The findings also demonstrate a difference of less than 5% from the conventional DTA models. In addition, they show that the maximum log-likelihood (MLL) model has a significantly better performance than the multi-sentence logit function (MLF). After validating the proposed solution method, the large-scale road network in the city of Yazd, as one of the metropolises in Iran, is analyzed, using the method concerned and the DTA is performed at different times of the day and night.