A Convolutional Neural Network for Transportation Mode Detection Based on Smartphone Platform

Xiaoyuan Liang, Guiling Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Scopus citations

Abstract

Knowledge of people's transportation mode is important in many civilian areas, such as urban transportation planning. Current methodologies in collecting travelers' transportation modes are costly and inaccurate. The increasing sensing and computing capabilities of smartphones and their high penetration rate enable automatic transportation mode detection. This paper designs and implements a light-weight and energy-efficient transportation mode detection application only using the accelerometer sensor on smartphones. In this application, we collect accelerometer data in an efficient way and build a convolutional neural network to determine transportation modes. Different architectures and different classification methods are tested within our convolutional neutral networks in our tests and the best combination is selected for this transportation mode detection application. Performance evaluation shows that the proposed convolutional neural network can achieve the highest accuracy in detecting transportation modes.

Original languageEnglish (US)
Title of host publicationProceedings - 14th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, MASS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages338-342
Number of pages5
ISBN (Electronic)9781538623237
DOIs
StatePublished - Nov 14 2017
Event14th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, MASS 2017 - Orlando, United States
Duration: Oct 22 2017Oct 25 2017

Publication series

NameProceedings - 14th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, MASS 2017

Other

Other14th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, MASS 2017
CountryUnited States
CityOrlando
Period10/22/1710/25/17

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering
  • Instrumentation
  • Electrical and Electronic Engineering

Keywords

  • accelerometer
  • deep learning
  • transportation mode detection

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