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Fuzzy — Genetic algorithm approach to generate an optimal meta-architecture for a smart, safe & efficient city transportation system of systems

A city's transportation infrastructure deeply affects its citizens' quality of life - from the freshness of food to the amount of frustration felt while commuting to work. Providing an optimal infrastructure has the possibility of dramatically improving the population's well-being. Since the transportation infrastructure is a system-of-systems (SoS) [1], it may be modelled and optimized for a given set of objectives [2]. In this manner, the city resources can be optimized for multiple objectives such as commute time, overall throughput, and sustainability. This is made possible by using a fuzzy assessor to map the individual objectives into a single overall fitness value that is optimized by a genetic algorithm (GA). The value of the individual objectives is determined by the selected systems which are each characterized by a set of characteristics (cost, CO2 emissions, etc.), capabilities, and interfaces.