Towards Recognising Individual Behaviours from Pervasive Mobile Datasets in Urban Spaces

Abstract

Mobile phone network data, routinely collected by its providers, possess very valuable encoded information about human behaviors. Intensive tourist activities in urban spaces bring smartness via mobile phone fingerprints into the understanding of an urban ecosystem. Due to the diverse processes that govern mobile communication, mining the geolocations of individuals seems to be non-trivial, tedious, and even irregular, which can lead to an incomplete trajectory. Enriching trajectories with infrastructural facilities is another challenge. We provide a unified approach, comprised of both informal and formal elements, to obtain a common framework, which maps pervasive datasets into a collection of individual patterns in urban spaces, to obtain context-enhanced trajectory reconstructions. Through the algorithmization of the approach, we acquire a study that provides new insights on individual and anonymized tourist behaviors. In order to obtain individual behaviors, it is necessary to carry out an arduous extraction process. We propose a multi-agent system architecture and predefined message streams, which are transported on a message-broker platform. We also propose all of the basic algorithms that compose the prototype of the entire multi-agent system. All algorithms were formally analyzed due to termination and time complexity. System evaluation, together with a few basic experiments, was also carried out. The performance evaluation results authenticate system feasibility, credibility, and vitality. Those factors prove its effectiveness and the possibility to build the target system, whilst supporting every urban ecosystem. The system would also strongly influence municipal services to understand urban context and operate more effectively in order to support tourist activities to become safer and more comfortable.

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