Representation learning of rare temporal conditions for travel time prediction

TL;DR

This paper introduces a vector-space encoding model for rare temporal conditions to improve travel time prediction accuracy, addressing data scarcity and heterogeneity issues in urban and suburban areas.

Contribution

It proposes a novel vector-space model for encoding rare temporal conditions, enabling more effective representation learning for travel time prediction.

Findings

Enhanced prediction accuracy over baselines using the new encoding.

Effective representation of heterogeneous and scarce temporal data.

Applicable to urban and suburban travel time forecasting.

Abstract

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.