Estimation model of origin and destination matrices in the dynamic assignment model under congestion conditions

The process of estimating the origin and destination matrices is that first there is an initial matrix that is given to the dynamic assignment network with the help of transims software so that the dynamic volumes are obtained and compared with the observed volumes in reality and their differences are corrected in later steps. Most studies to date have focused on providing new methods for problem solving, and due to the complexities of discussing the implementation of origin and destination matrix estimation in the real environment, less attention has been paid to it. In this research, modeling of this bi-level process in the form of a case study of Waterbury City (due to the availability of data and congested traffic network), dynamic assignment using transims software that is powerful in various transportation discussions and also has the ability to make changes to the program. The origin and destination matrix estimation in saturation conditions is performed using the Kalman filter algorithm and the least squares error approach (widely used and powerful in large-scale and saturation networks), as well as coding the entire origin and destination matrix estimation process. In the language of C++, is the innovation of this research. At the end of the iterative steps, the amount of arc volume error to confirm the process of estimating the source and destination matrix is obtained using the least squares error method, which is used to apply the process confirmation condition, the optimal source and destination matrix is obtained that close The results of the volumes observed in the real environment