Analysis of Mobility Patterns Based on LifeMap Monitoring

Abstract

With the rapid development of social networks and communication technologies, user’s mobility and their precise location information have become valuable resources for location based services and networking planning and management tasks. As the collection of user movement data is becoming much easier with smart phones and mobile devices, significant research efforts have been dedicated to analyse mass data and extract human mobility patterns and to predict human movements. These studies can serve for example for more efficient networking resources allocation and better location-based services. However, traditional mobility models do not take the regularity of people’s lifestyles into account and thus they might not be fully suitable for predicting human movements in the context of what is called today ”smart city”. Human movements are shaped and influenced heavily by social media and their work and daily lifestyles. The goal of this thesis has been to analyse the relationship between daily lifestyle and human mobility patterns based on data collected through LifeMap monitoring and to improve the existing mobility models in order to precisely predict human movements.