The Use of Simulation in Urban Modelling

The impact caused by the fast urban growth and by the occupation of ambient protection areas, demand efficient problem evaluation tools that may be capable to give support in the process of cities territorial, social and economics planning in a short and average stated period. One of the most serious problems of several cities is the disordered urban expansion, which becomes worse because of the lack of planning and specific strategies for this control. Urban systems are becoming ever larger and increasingly complex as urban economies, social and political structures and norms, and transportation and other infrastructure systems and technologies involved. We intent to provide a reasonable understanding of the context and objectives for urban simulation modelling, the limitations and challenges of urban simulation models, the design choices involved in developing operational models, and how such models are applied. The process of urban growth, however, is complex and difficult to model, due to the great number of operating actors in the city and the integrated landscape in different scales. The cities size changes all the time and all of possible rules that could be established are extremely complex (Allen, 1997, and Wu, 2002). To simulate operations and reactions of these real world processes, models of urban environment and the involved actors, are used to assist in exploration of the hypotheses, analysing the ambient processes and giving some answers about urban changes. To deal with these changes it is necessary to know the processes that caused them and identify the conditions. It is important to know how the changes of a city can occur. Cities are non linear complex systems, and their characteristics are difficult to be modelled by conventional methods (static and linear). New strategies have been developed for this class of problems. New methods of system modelling must be used in modelling and simulating urban phenomenon. Among others, cellular automata and multi-agent systems are being used successfully in cities simulation. CA is simple and is a well established method. Since the 1980’s, the development of discrete choice modelling and the emergence of cellular automata and multi-agent simulation techniques have created a proliferation of modelling approaches. We discuss each of these approaches below, and the supporting role of Geographic Information Systems and the integration of several of these approaches in the design of urban simulation. In this work, a CA urban simulator model will be presented. 8