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Combining Bayesian Inference and Clustering for Transport Mode Detection from Sparse and Noisy Geolocation Data

  • Danya BachirEmail author
  • Ghazaleh Khodabandelou
  • Vincent Gauthier
  • Mounim El Yacoubi
  • Eric Vachon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11053)

Abstract

Large-scale and real-time transport mode detection is an open challenge for smart transport research. Although massive mobility data is collected from smartphones, mining mobile network geolocation is non-trivial as it is a sparse, coarse and noisy data for which real transport labels are unknown. In this study, we process billions of Call Detail Records from the Greater Paris and present the first method for transport mode detection of any traveling device. Cellphones trajectories, which are anonymized and aggregated, are constructed as sequences of visited locations, called sectors. Clustering and Bayesian inference are combined to estimate transport probabilities for each trajectory. First, we apply clustering on sectors. Features are constructed using spatial information from mobile networks and transport networks. Then, we extract a subset of \(15\%\) sectors, having road and rail labels (e.g., train stations), while remaining sectors are multi-modal. The proportion of labels per cluster is used to calculate transport probabilities given each visited sector. Thus, with Bayesian inference, each record updates the transport probability of the trajectory, without requiring the exact itinerary. For validation, we use the travel survey to compare daily average trips per user. With Pearson correlations reaching 0.96 for road and rail trips, the model appears performant and robust to noise and sparsity.

Keywords

Mobile phone geolocation Call Detail Records Trajectory mining Transport mode Clustering Bayesian inference Big Data 

Notes

Acknowledgments

This research work has been carried out in the framework of IRT SystemX, Paris-Saclay, France, and therefore granted with public funds within the scope of the French Program “Investissements d’Avenir”. This work has been conducted in collaboration with Bouygues Telecom Big Data Lab.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Danya Bachir
    • 1
    • 2
    • 3
    Email author
  • Ghazaleh Khodabandelou
    • 2
  • Vincent Gauthier
    • 2
  • Mounim El Yacoubi
    • 2
  • Eric Vachon
    • 3
  1. 1.IRT SystemXPalaiseauFrance
  2. 2.SAMOVAR, Telecom SudParis, CNRS, Université Paris SaclayParisFrance
  3. 3.Bouygues Telecom Big Data LabMeudonFrance

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