Graph-ResNets for short-term traffic forecasts in almost unknown cities
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Author / Producer
Date
2020-08
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
The 2019 IARAI traffic4cast competition is a traffic forecasting problem based on traffic data from three cities that are encoded as images. We developed a ResNet-inspired graph convolutional neural network (GCN) approach that uses street network-based subgraphs of the image lattice graphs as a prior. We train the Graph-ResNet together with GCN and convolutional neural network (CNN) benchmark models on Moscow traffic data and use them to first predict the traffic in Moscow and then to predict the traffic in Berlin and Istanbul. The results suggest that the graph-based models have superior generalization properties than CNN-based models for this application. We argue that in contrast to purely image-based approaches, formulating the prediction problem on a graph allows the neural network to learn properties given by the underlying street network. This facilitates the transfer of a learned network to predict the traffic status at unknown locations.
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Publication status
published
Book title
Proceedings of Machine Learning Research, NeurIPS 2019 Competition and Demonstration Track, 8-14 December 2019, Vancouver, CA
Journal / series
Volume
123
Pages / Article No.
153 - 163
Publisher
PMLR
Event
NeurIPS 2019 Competition and Demonstration Track (NeurIPS 2019)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Machine Learning; Graph Neural Networks; Traffic forecasting
Organisational unit
03901 - Raubal, Martin / Raubal, Martin
Notes
Code available at: https://github.com/mie-lab/traffic4cast