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- W2897430952 abstract "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." @default.
- W2897430952 created "2018-10-26" @default.
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- W2897430952 date "2018-07-01" @default.
- W2897430952 modified "2023-10-17" @default.
- W2897430952 title "Activity Recognition for a Smartphone and Web-Based Human Mobility Sensing System" @default.
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- W2897430952 doi "https://doi.org/10.1109/mis.2018.043741317" @default.
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