چكيده به لاتين
Analysis and design of transportation systems requires estimating current travel demand and predicting future travel demand. These estimates and forecasts can be obtained with the help of various types of information origins and statistical processes. Travel request information for the area is stored in a matrix called the origin and destination matrix. The process of estimating origin and destination matrices in dynamic assignment models under saturated conditions is that a bi-level iterative process is used. First, there is an initial origin and destination matrix that is given to the dynamic assignment network using the transims software, and the resulting dynamic volumes are compared with the observed volumes in reality, and their differences are corrected in the following steps by various methods. It is, in fact, an iterative bi-level process between dynamic assignment and estimation of origin and destination matrices. Most of the studies that have been done so far in discussing the optimal origin and destination matrix estimation have focused on providing new methods to solve the origin and destination matrix estimation problem and due to the complexities of discussing the implementation of origin and destination matrix estimation in real environment, It has received less attention. The disadvantage of these studies is that many of them are not well implemented in software environments and therefore not feasible, and the importance of this research is from the perspective that it is usually used to calibrate models and its implementation is important. 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 under saturation conditions has been 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 link volume error is calculated to confirm the process of estimating the origin and destination matrices using the least squares error method. The condition for finishing the work and reaching the estimated origin and destination matrix is that the error values of the arc volume are less than the assumed value 5 and the optimal origin and destination matrix is obtained in which the arc volume values obtained from it, Match the values of the link observations. According to the formula for calculating the error of the volume of links and available data, the amount of error is considered equal to 5, which is equivalent to a difference of about 3000 units of volumes (0.01 of the total volume observed in the 100 links studied (384000)). Also, the maximum value of the link volume error, according to the data in this study, is theoretically 562.8, which is less than the results obtained, and indicates the confirmation of the outputs obtained from the origin and destination matrix estimation problem. . In discussing the origin and destination matrix estimation, it is especially important to pay attention to the errors in the primary research data because these errors can occur during the census and counting of traffic volumes or by data analysis tools and programs. However, these errors affect the results of estimating the origin and destination matrices, and considering these errors, the results show that the link volume error decreases each time the origin and destination matrix estimation problem is repeated and can be added to them. Fit a line with a downward slope. Finally, according to the assumed value of 5 as a criterion for confirming the process of estimating the origin and destination matrix, the link volume error after 23 repetitions (100 links studied), has reached the desired threshold.
