الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Urbanists move forward in our efforts to make cities more humane, livable, responsive, and resilient. Yet, the planning process of an ”international smart city ”will appear to be a utopian exercise, since cities are not all the same, and even the most creative typologies can never have a global influence. Each city have unique characteristics, context, information and data. The rapid changing relationship between citizens, city, and its institutions, causes a reform in data circumstances each minute. This let us wonder the capability of urbanists and decision makers to review, structure, analyze and take decisions upon big amount of data and challenges. Big data is term refer to huge data sets, have high Velocity, high Volume, and high Variety. Big Data through decision support systems (DSS) can manage cities planning, transportation demands, budgets, goals, and regulations. Also, it can manage different stakeholders and minority groups needs and requirements, while adapting to environmental, economical, and social current issues. Transportation planning and decision-making faces rapid urbanization challenges in both developed and developing countries, since urban planning process and decision makers depends essentially on data gathering and analysis. The research aims to discuss the opportunity to enhance the decision-making quality by reviewing the approaches of urban planning process models and incorporating them with big data and promoting the importance of the relationship between recording human behavior and efficient city planning. This could be achieved by creating a line of connection between urban planning process models, big data (BD), Decision support system (DSS). Also explores the prospects and potentials of BDT and DSS applications in urban planning by examining case studies in visualizing big data for decision support in urban planning will discussed. Moreover, a study will be discussed in using big data through DSS in public transportation planning in Egypt. This may be accomplished by acquiring data through transit apps and crowdsourcing concepts. Therefore, A comparative approach on using transit mobile apps’ feature and data usage in enhancing formal & informal transportation. Transit apps as: Uber, Careem, Didi, Indriver, Waslni, Mwasalat Misr and Swvl are examined. Finally, commenting on still existing issues and proposing recommendations for a new app development regarding the previous working apps. for sprinting Big data-based decision support system in transportation in Egyp.