چكيده به لاتين
To make informed decisions about transportation infrastructure planning, planners and engineers must be able to understand the response of transportation demand to changes in the characteristics of the transportation system and to changes in the characteristics of the people who use the transportation system. , to predict For this purpose, travel demand models are used. The first generation of demand models consists of travel-based models that are called four-stage models. Base activity models were created due to the insufficient accuracy of prediction of the four models in the review of policy planning. The activity-based approach is more advanced in cases such as considering travel as a demand arising from participation in the activity, real time and place limitations, relationships between activities and trips of a person, and relationships between family members, which leads to realistic presentation. The effect of the conditions on the activities and travel choices leads. In this research, in order to predict the demand for travel as an activity-based model, first the car ownership model was derived based on the data of the study area of Washington DC, then some software models were modified by the models made in previous studies according to the study area. . In the next step, the data related to the constructed population, persons, households, traffic areas, land use information and resistance matrices between the areas related to the city of Washington, DC, were entered into the Sim activity software. In the next step, the travel information obtained from the software was compared with the actual travel data in the study area and also with the number of trips of the first version of Act Gen software. The comparisons of travel matrices indicate that the concentration of trips according to the output of SIM activity software models is higher than the actual travel data, and the travel matrix of Actgen software gives more dispersion. And also in the number of trips based on different travel methods and travel goals, we see the closeness of the outputs of wire activity models and real data. Real basic information about the distribution of trips based on different age groups, income groups, household size, number of cars, population gender distribution and the approximate proximity of these data to the outputs of the number of trips implemented by the software models show the accuracy of the input information. And it has high analysis power of software models and similar modified models based on the studied range.
