Public transportation mode detection from cellular data

Guanyao Li, Chun Jie Chen, Sheng Yun Huang, Ai Jou Chou, Xiaochuan Gou, Wen Chih Peng, Chih Wei Yi

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

10 Scopus citations


Public transportation is essential in people's daily life and it is crucial to understand how people move around the city. Some prior works have exploited GPS, Wi-Fi or bluetooth to collect data, in which extra sensors or devices were needed. Other works utilized data from smart card systems. However, some public transportation systems have their own smart card system and the smart card data cannot include all kinds of transportation modes, which makes it unsuitable for our study.Nowadays, each user has his/her own mobile phones and from the cellular data of mobile phone service providers, it is possible to know the uses' transportation mode and the fine-grained crowd flows. As such, given a set of cellular data, we propose a system for public transportation mode detection, crowd density estimation, and crowd flow estimation. Note that we only have cellular data, no extra sensor data collected from users' mobile phones. In this paper, we refer to some external data sources (e.g., the bus routing networks) to identify transportation modes. Users' cellular data sometimes have uncertainty about user location information. Thus, we propose two approaches for different transportation mode detection considering the cell tower properties, spatial and temporal factors. We demonstrate our system using the data from Chunghwa Telecom, which is the largest telecommunication company in Taiwan, to show the usefulness of our system.

Original languageEnglish (US)
Title of host publicationCIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Number of pages4
ISBN (Electronic)9781450349185
StatePublished - Nov 6 2017
Event26th ACM International Conference on Information and Knowledge Management, CIKM 2017 - Singapore, Singapore
Duration: Nov 6 2017Nov 10 2017

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings
VolumePart F131841


Conference26th ACM International Conference on Information and Knowledge Management, CIKM 2017

Bibliographical note

Publisher Copyright:
© 2017 Association for Computing Machinery.


  • Crowd density and flow estimation
  • Smart cities
  • Transportation mode detection
  • Urban computing

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)


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