PhD thesis title: Merging data sources to assess modal shift potential in urban areas: a comparative analysis
Supervision: Patrick Bonnel et Caroline Bayart Reasearch Topix: Urban Mobility Interaction
Keywords: Aggregated Mobile Phone Data, Passive Data, Data Fusion, Modal shift
Research Description: Understanding people’s mobility behavior is a key to developing effective and efficient public policies to encourage modal shift. Passive data sources fusion, including mobile phone data, could provide accurate and up-to-date knowledge on this matter. The first objective of the project is therefore to analyze the available data sources to assess their relevance, and then to develop a methodology for merging these data sources to reconstruct origin-destination matrices by mode. The results obtained will be compared with other data sources, such as Household Surveys, in order to test the fusion process. In the second part of the work, the axes with the greatest potential for modal shift will be identified (high volume of travel, low share of carbon-free modes, for example), based on the reconstructed flows by mode. The methodological challenge will lie in developing new indicators. The approach will be applied to both Lyon and Rouen (France) to check that it is reproducible.
Education:
École polytechnique (France), Master of Science in Engineering « Cycle Ingénieur Polytechnicien » (2018-2021)
Polytechnique Montréal (Canada), Master of Engineering, Civil Engineering, Specialization in Transportation, (2021-2022)
École des Ponts ParisTech et AgroParisTech (France), Mastère Spécialisé Politiques et Actions Publiques pour le Développement Durable (2022-2023)
Academic Work:
Potentiel du Vélo à Assistance Electrique (research internship at Polytechnique Montréal)
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