  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.
