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      Towards a better understanding and behavior recognition of inhabitants in smart cities. A public transport case

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          Abstract

          The idea of modern urban systems and smart cities requires monitoring and careful analysis of different signals. Such signals can originate from different sources and one of the most promising is the BTS, i.e. base transceiver station, an element of mobile carrier networks. This paper presents the fundamental problems of elicitation, classification and understanding of such signals so as to develop context-aware and pro-active systems in urban areas. These systems are characterized by the omnipresence of computing which is strongly focused on providing on-line support to users/inhabitants of smart cities. A method of analyzing selected elements of mobile phone datasets through understanding inhabitants' behavioral fingerprints to obtain smart scenarios for public transport is proposed. Some scenarios are outlined. A multi-agent system is proposed. A formalism based on graphs that allows reasoning about inhabitant behaviors is also proposed.

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          Most cited references 6

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          Understanding individual human mobility patterns

          Despite their importance for urban planning, traffic forecasting, and the spread of biological and mobile viruses, our understanding of the basic laws governing human motion remains limited thanks to the lack of tools to monitor the time resolved location of individuals. Here we study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six month period. We find that in contrast with the random trajectories predicted by the prevailing Levy flight and random walk models, human trajectories show a high degree of temporal and spatial regularity, each individual being characterized by a time independent characteristic length scale and a significant probability to return to a few highly frequented locations. After correcting for differences in travel distances and the inherent anisotropy of each trajectory, the individual travel patterns collapse into a single spatial probability distribution, indicating that despite the diversity of their travel history, humans follow simple reproducible patterns. This inherent similarity in travel patterns could impact all phenomena driven by human mobility, from epidemic prevention to emergency response, urban planning and agent based modeling.
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            Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome

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              Cellular Census: Explorations in Urban Data Collection

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                Author and article information

                Journal
                23 April 2015
                2015-06-20
                Article
                10.1007/978-3-319-19369-4_22
                1504.06044

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Proceedings of 14th International Conference on Arificial Inteligence and Soft Computing (ICAISC 2015), 14-18 June, 2015, Zakopane, Poland; Lecture Notes in Computer Science, vol. 9120, pp.237-246. Springer Verlag 2015
                cs.CY

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