چكيده به لاتين
Geographical expansion of the cities along with their excessive population, urbanization and car ownership growth have caused the negative consequences of transportation including traffic congestion and air pollution. It is inevitable necessary to use the efficient tools and methods to solve these two urban problems based on the transportation policies’ impacts evaluation.
Transportation is, undoubtedly, a multidimensional, complicated and dynamic phenomenon, and System Dynamic (SD) is one of the most powerful and efficient tools to solve these types of problems. This research aims to develop a system dynamics model to analyze the urban transportation performance under the effect of various and relevant policies through the traffic congestion, air pollution, and fuel consumption indicators as well as different modal shares. The inputs of the model are transportation infrastructure characteristics and transportation costs, affected by the main transportation policies, also some variables influenced by the national level policies and economic conditions, such as fuel price and per capita income.
Having used various and relevant variables and modeling their interaction, this research provides a dynamic, integrated, comprehensive, and multimodal model with the capability to evaluate the impacts of 6-combined transportation related policies: public transportation development including the number and length of lines, fleet, and stations; extension of the road network; active transportation enhancement; parking development and pricing; travel demand management, TDM, including car traffic restriction, teleworking, distance learning, etc.; and transit oriented development, TOD, in three aspects contains high density, mix land use and the bike travel mode capability in the metro station areas; plus the two factors of fuel price and per capita income.
The developed model is validated against the available empirical data of Tehran Transportation, the case study of this research, and used to evaluate the urban transportation performance of it for TTMP base year, 2021, and long-term horizon, 2041. The model’s outputs are compared to the same results of pre-existing four-step travel demand model for these two years. In the following, the SD model is applied to evaluate the scenarios of Do Nothing, TTMP, and the TTMP enhanced by TOD factors improvement in 2041, and the results are presented. The developed SD model of this research, after validation with historical Tehran transportation data, employed to evaluate 20 policy-based scenarios for the year of 2041 and concluded that: it is necessary to adopt a combination of policies including public transportation development, TDM, TOD and active transportation to resolve the negative impacts of inefficient transportation system performance.
