  Bicycle-sharing systems, which can provide shared bike usage services for the
public, have been launched in many big cities. In bicycle-sharing systems,
people can borrow and return bikes at any stations in the service region very
conveniently. Therefore, bicycle-sharing systems are normally used as a
short-distance trip supplement for private vehicles as well as regular public
transportation. Meanwhile, for stations located at different places in the
service region, the bike usages can be quite skewed and imbalanced. Some
stations have too many incoming bikes and get jammed without enough docks for
upcoming bikes, while some other stations get empty quickly and lack enough
bikes for people to check out. Therefore, inferring the potential destinations
and arriving time of each individual trip beforehand can effectively help the
service providers schedule manual bike re-dispatch in advance. In this paper,
we will study the individual trip prediction problem for bicycle-sharing
systems. To address the problem, we study a real-world bicycle-sharing system
and analyze individuals' bike usage behaviors first. Based on the analysis
results, a new trip destination prediction and trip duration inference model
will be introduced. Experiments conducted on a real-world bicycle-sharing
system demonstrate the effectiveness of the proposed model.
