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A need to understand and predict vehicles' behavior underlies both public andprivate goals in the transportation domain, including urban planning andmanagement, 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 beachesare most popular in the summer. Despite this principle, we note that recentstudies on a popular benchmark dataset from Porto, Portugal have found, atbest, only marginal improvements in predictive performance from incorporatingtemporal information. We propose an approach based on hypernetworks, a variantof meta-learning ("learning to learn") in which a neural network learns tochange its own weights in response to an input. In our case, the weightsresponsible for destination prediction vary with the metadata, in particularthe time, of the input trajectory. The time-conditioned weights notably improvethe model's error relative to ablation studies and comparable prior work, andwe confirm our hypothesis that knowledge of time should improve prediction of avehicle's intended destination.

Meta-Learning over Time for Destination Prediction Tasks