چكيده به لاتين
Given the importance of transportation in the growth and development of countries today, along with the increase in travel, problems and challenges in this area have also expanded in countries. Urban traffic issues, environmental pollution, and other urban problems have led to the need for transportation planning, especially models for solving these problems, to expand. Travel demand prediction models are one of the most widely used models by researchers worldwide, and in these models, the economic and social characteristics of individuals are used as input. On the other hand, sustainable urban transportation development requires an increase in the use of public transportation, considering the current system's supply and facilities. As a result, this research aims to create a model with a direct approach to estimating universal transportation demand because, in the past, universal transportation demand was regulated with a proportional approach to the total share of travel demand in the areas under study, and less research focused on directly understanding the factors affecting universal transportation demand. Additionally, in the past, factors such as income and population of regions were used as the basis for building transportation demand models, and fewer factors affecting travel distribution or allocation were examined because in traditional models (4-step models), each of the travel production, distribution, mode choice, and assignment models were built separately, and in this regard, the influential parameters in each model had less overlap. However, modern modeling approaches aim to involve broader parameters and assess the cumulative impact of factors in models alongside each other. This research aims to develop a model for directly estimating urban household's universal transportation demand based on national housholeds travel survey. This requires not only focusing on economic and social parameters but also incorporating destination-specific distribution factors such as the number of students in households, as well as infrastructure factors affecting travel allocation, such as the length of universal transportation infrastructure in the studied areas, into the modeling process.
