A Deep Learning Model for Transportation Mode Detection Based on Smartphone Sensing Data

Document Type

Article

Publication Date

12-1-2020

Abstract

Understanding people's transportation modes is beneficial for empowering many intelligent transportation systems, such as supporting urban transportation planning. Yet, current methodologies in collecting travelers' transportation modes are costly and inaccurate. Fortunately, the increasing sensing and computing capabilities of smartphones and their high penetration rate offer a promising approach to automatic transportation mode detection via mobile computation. This paper introduces a light-weighted and energy-efficient transportation mode detection system using only accelerometer sensors in smartphones. The system collects accelerometer data in an efficient way and leverages a deep learning model to determine transportation modes. Different architectures and classification methods are tested with the proposed deep learning model to optimize the system design. Performance evaluation shows that the proposed new approach achieves a better accuracy than existing work in detecting people's transportation modes.

Identifier

85097230073 (Scopus)

Publication Title

IEEE Transactions on Intelligent Transportation Systems

External Full Text Location

https://doi.org/10.1109/TITS.2019.2951165

e-ISSN

15580016

ISSN

15249050

First Page

5223

Last Page

5235

Issue

12

Volume

21

Grant

CMMI-1844238

Fund Ref

National Science Foundation

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