Over the last years, there has been an increase in the rural exodus. The search for better living conditions has led the population to move from rural areas to large urban centers. This growth of cities contributed to a serious set of problems, such as virus spreads (human and electronic), traffic congestion, environmental impacts, inadequacy of public transport, and to make some tasks yet more complicated, such as urban planning and traffic forecasting. Understanding and predicting the mobility patterns of individuals, and places frequented by them, will be essential to combat these problems.
Within the context of the TICE.Mobility project the information about WiFi and GSM networks, obtained using smartphones, is used as a proxy to study the mobility patterns of users in order to help them identify ways of reducing their transportation impact to pollution and to their own budget.
The purpose of this dissertation is to automatically build maps that represent the mobility habits of individuals. These personal mobility maps, should reflect the mobility patterns, and should be represented by a graph which nodes would be used to represent the places the user visits and its edges would represent displacements among places. Through these maps, it is expected that people rectify some of their mobility practices that are less efficient.
The main goals of this work are as follows:
· Identify and characterize the more frequent journeys between places (flows, transportation modes, distances and travel times, etc..) and represent them as edges of the graph;
This presentation focuses on the process developed to discover relevant places of users (data collection > data processing > place learning), results obtained and validation of places, stays and entrance and departure times.
Ângelo Conde has a degree in Information Systems and Technologies (2009) and is currently a student in the MSc in Information Systems Engineering and Management at the University of Minho. He is working on his thesis under the guidance of Adriano Moreira. His current research interests include mobile sensing systems, human mobility, mobility prediction and human mobility modeling.