Activity Recognition for a Smartphone and Web-Based Human Mobility Sensing System
Name
activity_recognition_arvix.pdf
Size
2.62 MB
Format
Adobe PDF
Checksum (MD5)
7b3b02a6ee552f26ec541a41af9a99ac
Author(s)
Kim, Youngsung
Ghorpade, Ajinkya
Zhao, Fang
Pereira, Francisco C.
Zegras, Pericles C
Ben-Akiva, Moshe E
Alternative Title
Activity recognition for a smartphone and web-based human mobility sensing system
Date Issued
July 2018
Journal
IEEE Intelligent Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Kim, Youngsung et al. “Activity Recognition for a Smartphone and Web-Based Human Mobility Sensing System.” IEEE Intelligent Systems 33, 4 (July 2018): 5–23 © 2018 IEEE
Version
Original manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/MIS.2018.043741317
