الفهرس 
Impact of urban morphology on pedestriansOnly 14 pages are availabe for public view
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Abstract
Cities are explained as complex human settlements. A city’s design is a complex undertaking that necessitates the definition of various aspects and the consideration of several performance criteria. Urban morphology is the Physical description of cities changing over the years. The generation of urban morphology to satisfy pedestrians is a multidimensional and complex process. Today, in our cities, the pedestrian suffers from neglect; as a result, we find cities lacking human dimension. Hence, generating urban morphology for pedestrians has an integrated process that includes various urban morphology approaches, which affect pedestrians. The evolution of the Computational Generation systems imposes to be credence during the generation process. Genetic Algorithms is one of the most powerful systems used in urban morphology generation. In the last five years, Much research targeted the application of genetic algorithms in urban morphology generation. As a result, many tools and software have been developed. However, there is an absence of a clear and integrated framework that can be used to generate urban morphology for pedestrians using a genetic algorithms system. This thesis introduces an integrated framework to be used in urban morphology generation for pedestrians. Besides, it introduces a grasshopper plugin for the parametric modelling process and calculates several urban morphology indicators. The framework and plugin have been used in generating walkable residential neighbourhoods in port said city. The proposed framework is based on nine generation objectives used to optimize the most suitable alternatives; enhancing thermal comfort index in summer and winter, increasing site accessibility, decreasing vehicular angular integration, increasing pedestrian connectivity, decreasing distance between land uses, increasing pedestrian permeability, and increasing floor space index. The final results show that the optimization process achieves high fitness values at four objectives, good fitness values at two objectives, and objectives aren’t fit enough. Eight of the most suitable alternatives are selected to be studied and compared. The results showed that the eight alternatives obtain high or good fitted values in almost objectives, and choose the most suitable alternatives for each objective.