Full text
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| Author(s): |
Vanessa da Silva Santos
Total Authors: 1
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| Document type: | Master's Dissertation |
| Press: | São Carlos. |
| Institution: | Universidade de São Paulo (USP). Escola de Engenharia de São Carlos (EESC/SBD) |
| Defense date: | 2005-09-27 |
| Examining board members: |
Antonio Nelson Rodrigues da Silva;
Lenise Grando Goldner;
Yaeko Yamashita
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| Advisor: | Antonio Nelson Rodrigues da Silva |
| Abstract | |
In conditions of extreme restrictions of resources for construction and maintainance of public facilities, the correct demand alocation is essential not only for the decision-making process, which involves their implementation and rational use, but also for reaching a significant share of the demand with a reasonable level of service and within the budget limits. That was the starting point for the definition of the objectives of this work, which are to model the population dynamics in a medium-sized city by using a cellular automata approach and multicriteria evaluation techniques and to simulate trip distribution patterns to a particular public facility, the EMEIs, i.e., schools for children between 4 and 6 years-old. The study starts with an application of two urban dynamics models in the city of São Carlos: a cellular automata model and a demographic model based on linear regression. A scenario of the year 2010 was built based on 1980, 1991, and 2000 census data. The cellular automata model was constructed in three fases: quantification of sprawl (definition of the total area added to the existing urban area), location of sprawl (alocation of areas of expansion), and differentiation of sprawl (definition of the population density in each cell of the new urban area). The model captured reasonably well the urban dynamics process in both location and differentiation of sprawl. The demand for EMEIs was then defined through the definition of a mathematical relationship between the total population and the target population. The population density predicted by the demographic model was calculated through a linear trend applied to historical data. It was significant in this case the occurrence of estimated values higher than the actual values mainly due to the restrictions of the urban area to its previous boundaries. The demand for EMEIs was also obtained using a linear trend, this time using specific data of the target population. Scenarios of trip distribution in the years 2000 and 2010 were created after the demand was modeled. The predictions of the models were very different, and the total demand estimation of the cellular automata model was higher than that obtained with the demographic model. When adjusting the demand to the existing supply in the year 2000, the trip distribution results were quite similar, although resulting in extremely different service rates. However, in a very likely scenario of supply growth, either through new or existing schools, the impacts on transportation would be better identified in the case of CA models, not only because they have produced higher demand estimates, but also because they have considered the inclusion of new areas into the modeled space (AU) | |