A Graph-based U-Net Model for Predicting Traffic in unseen Cities

• URL: http://arxiv.org/abs/2202.06725v1
• Date: Fri, 11 Feb 2022 09:11:04 GMT
• Title: A Graph-based U-Net Model for Predicting Traffic in unseen Cities
• Authors: Luca Hermes, Barbara Hammer, Andrew Melnik, Riza Velioglu, Markus Vieth, Malte Schilling
• Abstract summary: A way to represent traffic data is in the form of temporally changing heatmaps visualizing attributes of traffic, such as speed and volume. We propose to combine the U-Net architecture with graph layers which improves spatial generalization to unseen road networks.
• Score: 5.501569874656471
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Accurate traffic prediction is a key ingredient to enable traffic management like rerouting cars to reduce road congestion or regulating traffic via dynamic speed limits to maintain a steady flow. A way to represent traffic data is in the form of temporally changing heatmaps visualizing attributes of traffic, such as speed and volume. In recent works, U-Net models have shown SOTA performance on traffic forecasting from heatmaps. We propose to combine the U-Net architecture with graph layers which improves spatial generalization to unseen road networks compared to a Vanilla U-Net. In particular, we specialize existing graph operations to be sensitive to geographical topology and generalize pooling and upsampling operations to be applicable to graphs.

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