Online Travel Mode Identification Using Smartphones with Battery Saving Considerations

Xing Su, Hernan Caceres, Hanghang Tong, Qing He

Research output: Contribution to journalArticlepeer-review

47 Scopus citations

Abstract

Personal trips in modern urban society usually involve multiple travel modes. Recognizing a traveler's transportation mode is not only critical to personal context awareness in related applications but also essential to urban traffic operations, transportation planning, and facility design. While most current practice often leverages infrastructure-based fixed sensors or a Global Positioning System (GPS) for traffic mode recognition, the emergence of the smartphone provides an alternative promising way with its ever-growing computing, networking, and sensing power. In this paper, we propose a GPS-and-network-free method to detect a traveler's travel mode using mobile phone sensors. Our application is built on the latest Android platform with multimodality sensors. By developing a hierarchical classification method with an online learning model, we achieve almost 100% accuracy in a binary classification of wheeled/unwheeled travel modes and an average of 97.1% accuracy with all six travel modes (buses, subways, cars, bicycling, walking, and jogging). Our system (a) could adapt to each traveler's pattern by using the online learning model, and it performs significantly faster in computation than the offline model, and (b) works with a low sampling frequency for sensing so that it saves the smartphone battery.

Original languageEnglish (US)
Article number7426823
Pages (from-to)2921-2934
Number of pages14
JournalIEEE Transactions on Intelligent Transportation Systems
Volume17
Issue number10
DOIs
StatePublished - Oct 2016

Keywords

  • Mobile mining
  • Traffic mode identification
  • online learning
  • vehicle classification

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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