Online Travel Mode Identification Using Smartphones With Battery Saving Considerations

Xing Su, Hernan Caceres, Hanghang Tong, Qing He

Research output: Contribution to journalArticle

18 Citations (Scopus)

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)
JournalIEEE Transactions on Intelligent Transportation Systems
DOIs
StateAccepted/In press - Mar 7 2016

Fingerprint

Smartphones
Global positioning system
Subway cars
Sensors
Mobile phones
Sampling
Planning

ASJC Scopus subject areas

  • Automotive Engineering
  • Computer Science Applications
  • Mechanical Engineering

Cite this

Online Travel Mode Identification Using Smartphones With Battery Saving Considerations. / Su, Xing; Caceres, Hernan; Tong, Hanghang; He, Qing.

In: IEEE Transactions on Intelligent Transportation Systems, 07.03.2016.

Research output: Contribution to journalArticle

@article{d6262506753d43a8b7bbb72794a5c6c8,
title = "Online Travel Mode Identification Using Smartphones With Battery Saving Considerations",
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.",
author = "Xing Su and Hernan Caceres and Hanghang Tong and Qing He",
year = "2016",
month = "3",
day = "7",
doi = "10.1109/TITS.2016.2530999",
language = "English (US)",
journal = "IEEE Transactions on Intelligent Transportation Systems",
issn = "1524-9050",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Online Travel Mode Identification Using Smartphones With Battery Saving Considerations

AU - Su, Xing

AU - Caceres, Hernan

AU - Tong, Hanghang

AU - He, Qing

PY - 2016/3/7

Y1 - 2016/3/7

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84962524703&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84962524703&partnerID=8YFLogxK

U2 - 10.1109/TITS.2016.2530999

DO - 10.1109/TITS.2016.2530999

M3 - Article

AN - SCOPUS:84962524703

JO - IEEE Transactions on Intelligent Transportation Systems

JF - IEEE Transactions on Intelligent Transportation Systems

SN - 1524-9050

ER -