Individual mobility is driven by demand for activities with diverse spatiotemporal patterns, but
existing methods for mobility prediction often overlook the underlying activity patterns …

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 …

With the emergence of smartphones and location-based services, user mobility prediction
has become a critical enabler for a wide range of applications, like location-based …

For intelligent urban transportation systems, the ability to predict individual mobility is crucial
for personalized traveler information, targeted demand management, and dynamic system …

Predicting trip purpose from comprehensive and continuous smart card data is beneficial for
transport and city planners in investigating travel behaviors and urban mobility. Here, we …

Short-term trajectory prediction (StTP) for individual metro passengers is of great importance
in intelligent transportation systems and real-time security risk management. Existing …

Although automatically collected human travel records can accurately capture the time and
location of human movements, they do not directly explain the hidden semantic structures …

Although smart-card data were expected to substitute for conventional travel surveys, the
reality is that only a few automatic fare collection (AFC) systems can recognize an individual …

Over the last few years, cities have made available large volumes of smart card data that
shed light on the urban dynamics of transit users. This research uses metro card data from …

Passenger flow prediction is one of the most important issues in an urban subway system
toward smart cities, which can help cut a trip time, plan a trip route, and thus provide a …