  A need to understand and predict vehicles' behavior underlies both public and
private goals in the transportation domain, including urban planning and
management, ride-sharing services, and intelligent transportation systems.
Individuals' preferences and intended destinations vary throughout the day,
week, and year: for example, bars are most popular in the evenings, and beaches
are most popular in the summer. Despite this principle, we note that recent
studies on a popular benchmark dataset from Porto, Portugal have found, at
best, only marginal improvements in predictive performance from incorporating
temporal information. We propose an approach based on hypernetworks, a variant
of meta-learning ("learning to learn") in which a neural network learns to
change its own weights in response to an input. In our case, the weights
responsible for destination prediction vary with the metadata, in particular
the time, of the input trajectory. The time-conditioned weights notably improve
the model's error relative to ablation studies and comparable prior work, and
we confirm our hypothesis that knowledge of time should improve prediction of a
vehicle's intended destination.
