Issue No. 04 - Jul./Aug. (2018 vol. 33)
Youngsung Kim , Singapore-MIT Alliance for Research and Technology (SMART), Singapore
Ajinkya Ghorpade , Singapore-MIT Alliance for Research and Technology (SMART), Singapore
Fang Zhao , Singapore-MIT Alliance for Research and Technology (SMART), Singapore
Francisco C. Pereira , Technical University of Denmark
P. Christopher Zegras , Massachusetts Institute of Technology
Moshe Ben-Akiva , Massachusetts Institute of Technology
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.
data handling, Internet, learning (artificial intelligence), mobile computing, smart phones, transportation, travel industry, user interfaces
Y. Kim, A. Ghorpade, F. Zhao, F. C. Pereira, P. C. Zegras and M. Ben-Akiva, "Activity Recognition for a Smartphone and Web-Based Human Mobility Sensing System," in IEEE Intelligent Systems, vol. 33, no. 4, pp. 5-23, 2018.