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Alessandretti L, Karsai M, Gauvin L. User-based representation of time-resolved multimodal public transportation networks. R Soc Open Sci. 2016;3(7):160156doi: 10.1098/rsos.160156.
Alessandretti, L., Karsai, M., & Gauvin, L. (2016). User-based representation of time-resolved multimodal public transportation networks. Royal Society open science, 3(7), 160156. https://doi.org/10.1098/rsos.160156
Alessandretti, Laura, et al. "User-based representation of time-resolved multimodal public transportation networks." Royal Society open science vol. 3,7 (2016): 160156. doi: https://doi.org/10.1098/rsos.160156
Alessandretti L, Karsai M, Gauvin L. User-based representation of time-resolved multimodal public transportation networks. R Soc Open Sci. 2016 Jul 13;3(7):160156. doi: 10.1098/rsos.160156. eCollection 2016 Jul. PMID: 27493773; PMCID: PMC4968465.
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R Soc Open Sci. 2016 Jul 13;3(7):160156. doi: 10.1098/rsos.160156. eCollection 2016 Jul.

User-based representation of time-resolved multimodal public transportation networks.

Royal Society open science

Laura Alessandretti, Márton Karsai, Laetitia Gauvin

Affiliations

  1. Université de Lyon, ENS de Lyon, LIP, INRIA-CNRS-UMR 5668, IXXI, 69364 Lyon, France; Data Science Lab, ISI Foundation, Turin, Italy; Department of Mathematics, City University London, London EC1V 0HB, UK.
  2. Université de Lyon, ENS de Lyon , LIP, INRIA-CNRS-UMR 5668, IXXI, 69364 Lyon, France.
  3. Université de Lyon, ENS de Lyon, LIP, INRIA-CNRS-UMR 5668, IXXI, 69364 Lyon, France; Data Science Lab, ISI Foundation, Turin, Italy.

PMID: 27493773 PMCID: PMC4968465 DOI: 10.1098/rsos.160156

Abstract

Multimodal transportation systems, with several coexisting services like bus, tram and metro, can be represented as time-resolved multilayer networks where the different transportation modes connecting the same set of nodes are associated with distinct network layers. Their quantitative description became possible recently due to openly accessible datasets describing the geo-localized transportation dynamics of large urban areas. Advancements call for novel analytics, which combines earlier established methods and exploits the inherent complexity of the data. Here, we provide a novel user-based representation of public transportation systems, which combines representations, accounting for the presence of multiple lines and reducing the effect of spatial embeddedness, while considering the total travel time, its variability across the schedule, and taking into account the number of transfers necessary. After the adjustment of earlier techniques to the novel representation framework, we analyse the public transportation systems of several French municipal areas and identify hidden patterns of privileged connections. Furthermore, we study their efficiency as compared to the commuting flow. The proposed representation could help to enhance resilience of local transportation systems to provide better design policies for future developments.

Keywords: human dynamics; multimodal networks; public transportation

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