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.
CITATION STYLE
Yue, M., Sun, T., Wu, F., Wu, L., Xu, Y., & Shahabi, C. (2021). Learning a Contextual and Topological Representation of Areas-of-Interest for On-Demand Delivery Application. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12460 LNAI, pp. 52–68). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-67667-4_4
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