Simulated annealing: theory and applications
Simulated annealing: theory and applications
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
A fuzzy prediction model for calling communities
International Journal of Networking and Virtual Organisations
Identifying important places in people's lives from cellular network data
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
Estimating Origin-Destination Flows Using Mobile Phone Location Data
IEEE Pervasive Computing
Proceedings of the 4th ACM/IEEE International Conference on Information and Communication Technologies and Development
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Commuting matrices are key for a variety of fields, including transportation engineering and urban planning. Up to now, these matrices have been typically generated from data obtained from surveys. Nevertheless, such approaches typically involve high costs which limits the frequency of the studies. Cell phones can be considered one of the main sensors of human behavior due to its ubiquity, and as a such, a pervasive source of mobility information at a large scale. In this paper we propose a new technique for the estimation of commuting matrices using the data collected from the pervasive infrastructure of a cell phone network. Our goal is to show that we can construct cell-phone generated matrices that capture the same patterns as traditional commuting matrices. In order to do so we use optimization techniques in combination with a variation of Temporal Association Rules. Our validation results show that it is possible to construct commuting matrices from call detail records with a high degree of accuracy, and as a result our technique is a cost-effective solution to complement traditional approaches.