Distributed learning automata based approach to inferring urban structure via traffic flow

Traffic flow can be used as a reference for knowledge generation, which is highly important in urban planning. One of the significant applications of traffic data is decision making about the structure of roads connecting zones of a city. It leads us to an optimal connection between important areas like business centers, shopping malls, construction sites, residential complexes, and other parts of a city which is the motivation of this research. The main question is how to infer the optimal connectivity network considering the current structure of an urban area and time-varying traffic dynamics. Therefore a novel formulation is created in this paper to solve the optimization problem using available data. A proposed algorithm is presented to infer the optimal structure that is a distributed learning automata-based approach. A matrix called estimated optimal connectivity represents the favorite structure and it is optimized utilizing signals about the current system and traffic dynamics from the environment. Two types of data, including synthetic and real-world, are used to show the algorithm's ability. After many experiments, the algorithm showed capability of optimizing the structure by finding new paths connecting the most correlated areas.