Learning a Contextual and Topological Representation of Areas-of-Interest for On-Demand Delivery Application

A good representation of urban areas is of great importance in on-demand delivery services such as for ETA prediction. However, the existing representations learn either from sparse check-in histories or topological geometries, thus are either lacking coverage and violating the geographical law or ignoring contextual information from data. In this paper, we propose a novel representation learning framework for obtaining a unified representation of Area of Interest from both contextual data (trajectories) and topological data (graphs). The framework first encodes trajectories and graphs into homogeneous views, and then train a multi-view autoencoder to learn the representation of areas using a ranking-based loss. Experiments with real-world package delivery data on ETA prediction confirm the effectiveness of the model.

Automated summary

This paper proposes a novel representation learning framework to obtain a unified representation of Areas of Interest from both contextual data and topological data, and the effectiveness of the model is confirmed through experiments with real-world package delivery data on ETA prediction.