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A Comparative Evaluation of Urban Fabric Detection Techniques Based on Mobile Traffic Data

Angelo Furno 1, * Razvan Stanica 1 Marco Fiore 1, 2
* Corresponding author
1 URBANET - Réseaux capillaires urbains
Inria Grenoble - Rhône-Alpes, CITI - CITI Centre of Innovation in Telecommunications and Integration of services
Abstract : Mobile traffic data has been recently used to characterize the urban environment in terms of urban fabric profiles. While showing promising results, the existing urban fabric detection solutions are built without a clear understanding of the detection process chain. In this paper, we distinguish and analyze the different steps common to all urban profiling techniques. By evaluating the impact of each step of the process, we are able to propose a new solution that outperforms the state of the art techniques. Our approach uses the weekly periodicity of human activities, as well as a median-based filtering technique, resulting in a better clustering in terms of both coverage and entropy, as shown by results obtained on two large scale mobile traffic datasets covering the urban areas of Milan and Turin, in Italy.
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https://hal.inria.fr/hal-01246620
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Submitted on : Friday, December 18, 2015 - 6:19:08 PM
Last modification on : Wednesday, July 8, 2020 - 12:43:15 PM
Long-term archiving on: : Saturday, April 29, 2017 - 10:05:15 PM

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Angelo Furno, Razvan Stanica, Marco Fiore. A Comparative Evaluation of Urban Fabric Detection Techniques Based on Mobile Traffic Data. ASONAM 2015 - IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Aug 2015, Paris, France. ⟨hal-01246620⟩

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