TY - JOUR
T1 - Activity Recognition for a Smartphone and Web-based Human Mobility Sensing System
AU - Kim, Youngsung
AU - Ghorpade, Ajinkya
AU - Zhao, Fang
AU - Pereira, Francisco Camara
AU - Zegras, P. Christopher
AU - Ben-Akiva, Moshe
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
U2 - 10.1109/MIS.2018.043741317
DO - 10.1109/MIS.2018.043741317
M3 - Journal article
SN - 1541-1672
VL - 33
SP - 5
EP - 23
JO - IEEE Intelligent Systems
JF - IEEE Intelligent Systems
IS - 4
ER -