Predicting Personal Mobility with Individual and Group Travel Histories

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 our approach's effectiveness using a massive data set 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.

By: Giusy Di Lorenzo; Jonathan Reades; Francesco Calabrese; Carlo Ratti

Published in: RC25265 in 2012


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