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

Autor:	Hermes, Luca, Hammer, Barbara, Melnik, Andrew, Velioglu, Riza, Vieth, Markus, Schilling, Malte
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: About to be published in IJCNN Proceedings 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2202.06725 Zobrazit plný text záznamu View this record from Arxiv