Robust commuter movement inference from connected mobile devices

Baoyang Song, Hasan Poonawala, Laura Wynter, Sebastien Blandin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The preponderance of connected devices provides unprecedented opportunities for fine-grained monitoring of the public infrastructure. However while classical models expect high quality application-specific data streams, the promise of the Internet of Things (IoT) is that of an abundance of disparate and noisy datasets from connected devices. In this context, we consider the problem of estimation of the level of service of a city-wide public transport network. We first propose a robust unsupervised model for train movement inference from wifi traces, via the application of robust clustering methods to a one dimensional spatiooral setting. We then explore the extent to which the demand-supply gap can be estimated from connected devices. We propose a classification model of real-time commuter patterns, including both a batch training phase and an online learning component. We describe our deployment architecture and assess our system accuracy on a large-scale anonymized dataset comprising more than 10 billion records.

Original languageEnglish
Title of host publicationProceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018
EditorsFeida Zhu, Zhenhui Li, Hanghang Tong, Jeffrey Yu
Pages640-647
Number of pages8
ISBN (Electronic)9781538692882
DOIs
StatePublished - Feb 7 2019
Event18th IEEE International Conference on Data Mining Workshops, ICDMW 2018 - Singapore, Singapore
Duration: Nov 17 2018Nov 20 2018

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume2018-November
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Conference

Conference18th IEEE International Conference on Data Mining Workshops, ICDMW 2018
Country/TerritorySingapore
CitySingapore
Period11/17/1811/20/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

  • Public transport
  • classification
  • online learning
  • real-time estimation
  • unsupervised models

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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