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

Youngsung Kim, Ajinkya Ghorpade, Fang Zhao, Francisco Camara Pereira, P. Christopher Zegras, Moshe Ben-Akiva

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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
    Issue number4
    Pages (from-to)5-23
    Publication statusPublished - 2018


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