Activity Recognition for a Smartphone and Web-based Human Mobility Sensing System

Research output: Research - peer-reviewJournal article – Annual report year: 2018

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Activity-based models in transport modeling and prediction are built from a large number of observed trips and their purposes. However, data acquired through traditional interview-based travel surveys is often inaccurate and insufficient. Recently, a human mobility sensing system, called Future Mobility Survey (FMS), was developed and used to collect travel data from more than 1,000 participants. FMS combines a smartphone and interactive web interface in order to better infer users activities and patterns. This paper presents a model that infers an activity at a certain location. We propose to generate a set of predictive features based on spatial, temporal, transitional, and environmental contexts with an appropriate quantization. In order to improve the generalization performance of the proposed model, we employ a robust approach with ensemble learning. Empirical results using FMS data demonstrate that the proposed method contributes significantly to providing accurate activity estimates for the user in our travel-sensing application.
Original languageEnglish
JournalIEEE Intelligent Systems
Volume33
Issue number4
Pages (from-to)5-23
Number of pages19
ISSN1541-1672
DOIs
StatePublished - 2018
CitationsWeb of Science® Times Cited: 0
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