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The Quo Vadis submission at Traffic4cast 2019
- Publication Year :
- 2019
-
Abstract
- We describe the submission of the Quo Vadis team to the Traffic4cast competition, which was organized as part of the NeurIPS 2019 series of challenges. Our system consists of a temporal regression module, implemented as $1\times1$ 2d convolutions, augmented with spatio-temporal biases. We have found that using biases is a straightforward and efficient way to include seasonal patterns and to improve the performance of the temporal regression model. Our implementation obtains a mean squared error of $9.47\times 10^{-3}$ on the test data, placing us on the eight place team-wise. We also present our attempts at incorporating spatial correlations into the model; however, contrary to our expectations, adding this type of auxiliary information did not benefit the main system. Our code is available at https://github.com/danoneata/traffic4cast.<br />Comment: Extended abstract for the Traffic4cast competition from NeurIPS 2019
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.1910.12363
- Document Type :
- Working Paper