Representation learning of rare temporal conditions for travel time prediction
This addresses a challenge in urban and suburban transportation planning where limited historical data hinders accurate predictions, though it appears incremental as it builds on existing representation learning methods.
The paper tackles the problem of predicting travel time under rare temporal conditions like public holidays by developing a vector-space model for encoding these conditions, showing increased performance over baselines.
Predicting travel time under rare temporal conditions (e.g., public holidays, school vacation period, etc.) constitutes a challenge due to the limitation of historical data. If at all available, historical data often form a heterogeneous time series due to high probability of other changes over long periods of time (e.g., road works, introduced traffic calming initiatives, etc.). This is especially prominent in cities and suburban areas. We present a vector-space model for encoding rare temporal conditions, that allows coherent representation learning across different temporal conditions. We show increased performance for travel time prediction over different baselines when utilizing the vector-space encoding for representing the temporal setting.