A multi-scale approach to data-driven mass migration analysis
Mohammed N. Ahmed, Gianni Barlacchi, et al.
SoGood 2016
Understanding and predicting human mobility is a crucial component of a range of administrative activities, from transportation planning to tourism and travel management. In this paper we propose a new approach that predicts the location of a person over time based on both individual and collective behaviors. The system draws on both previous trajectory histories and the features of the region-in terms of geography, land use, and points of interest-which might be 'of interest' to travellers. We test the effectiveness of our approach using a massive dataset of mobile phone location events compiled for the Boston metropolitan region, and experimental results suggest that the predictions are accurate to within 1.35km and demonstrate the significant advantages of incorporating collective behavior into individual trip predictions. © 2012 Pion and its Licensors.
Mohammed N. Ahmed, Gianni Barlacchi, et al.
SoGood 2016
Bin Guo, Daqing Zhang, et al.
UbiComp 2011
Angela Sara Cacciapuoti, Francesco Calabrese, et al.
Pervasive and Mobile Computing
Xiaowen Dong, Dimitrios Mavroeidis, et al.
Data Mining and Knowledge Discovery