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Article Dans Une Revue Future Transportation Année : 2023

Identification of Aggregate Urban Mobility Patterns of Nonregular Travellers from Mobile Phone Data

Résumé

Over the last two decades, mobile phone data have appeared to be a promising data source for mobility analysis. The structure, abundance, and accessibility of call detail records (CDRs) make them particularly suitable for such use. However, their exploitation is often limited to estimating origin–destination matrices of a restricted part of the population: regular travellers. Although these studies provide valuable information for policymakers, their scope remains limited to this subpopulation analysis. In the present work, we develop a collective mobility reconstruction method adapted to nonregular travellers. The method relies on the notion of the detour ratio, which makes it robust to the lack of mobile phone data as well as its application to large instances (large and dense telecommunication networks). It is used to conduct a longitudinal analysis of the macroscopic mobility patterns in Santiago de Cali, Colombia, thanks to call detail data shared by communication provider CLARO as part of a research project conducted by Citepa, Paris, the Green City Big Data Project.
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Dates et versions

hal-04528797 , version 1 (13-05-2024)

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Manon Seppecher, Ludovic Leclercq, Angelo Furno, Thamara Vieira da Rocha, Jean-Marc André, et al.. Identification of Aggregate Urban Mobility Patterns of Nonregular Travellers from Mobile Phone Data. Future Transportation, 2023, 3 (1), pp.254-273. ⟨10.3390/futuretransp3010015⟩. ⟨hal-04528797⟩
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