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

Abstract

In transport modeling and prediction, trip purposes play an important role since mobility choices (e.g. modes, routes, departure times) are made in order to carry out specific activities. Activity based models, which have been gaining popularity in recent years, are built from a large number of observed trips and their purposes. However, data acquired through traditional interview-based travel surveys lack the accuracy and quantity required by such models. Smartphones and interactive web interfaces have emerged as an attractive alternative to conventional travel surveys. A smartphone-based travel survey, Future Mobility Survey (FMS), was developed and field-tested in Singapore and collected travel data from more than 1000 participants for multiple days. To provide a more intelligent interface, inferring the activities of a user at a certain location is a crucial challenge. This paper presents a learning model that infers the most likely activity associated to a certain visited place. The data collected in FMS contain errors or noise due to various reasons, so a robust approach via ensemble learning is used to improve generalization performance. Our model takes advantage of cross-user historical data as well as user-specific information, including socio-demographics. Our empirical results using FMS data demonstrate that the proposed method contributes significantly to our travel survey application.

Transport behavior-mining from smartphones: a review

“…The literature reviewed often works with location and user-agnostic features. In contrast, user- [60,126] and location-specific [96] data seem to enable more accurate classifications. Although results presented in the relevant literature are hardly comparable across studies, within each relevant study we find evidence about the positive contribution of user-and location-specific data on the performance of the classifiers [104].…”

Multiperson Interactive Activity Recognition Based on Interaction Relation Model

“…Xu et al [13] proposed technology of novel group activity recognition which is proposed based on multimodal relation representation with temporal-spatial attention. Kim et al [14] proposed a set of predictive features interbehavior relation based on spatial, temporal, transitional, and environmental contexts. Liu et al [15] proposed a deep fully connected relation model to learn the interactions between people.…”

Crowd intelligence for sustainable futuristic intelligent transportation system: a review

“…There are several algorithms for activity detection [221] of human and vehicle suggested in the literature. However, these algorithms fail most of the time because of GPS failure or GPS error.…”