  Individual mobility is driven by demand for activities with diverse
spatiotemporal patterns, but existing methods for mobility prediction often
overlook the underlying activity patterns. To address this issue, this study
develops an activity-based modeling framework for individual mobility
prediction. Specifically, an input-output hidden Markov model (IOHMM) framework
is proposed to simultaneously predict the (continuous) time and (discrete)
location of an individual's next trip using transit smart card data. The
prediction task can be transformed into predicting the hidden activity duration
and end location. Based on a case study of Hong Kong's metro system, we show
that the proposed model can achieve similar prediction performance as the
state-of-the-art long short-term memory (LSTM) model. Unlike LSTM, the proposed
IOHMM model can also be used to analyze hidden activity patterns, which
provides meaningful behavioral interpretation for why an individual makes a
certain trip. Therefore, the activity-based prediction framework offers a way
to preserve the predictive power of advanced machine learning methods while
enhancing our ability to generate insightful behavioral explanations, which is
useful for enhancing situational awareness in user-centric transportation
applications such as personalized traveler information.
