Michael Kopp, David Kreil, Moritz Neun, David Jonietz, Henry Martin, Pedro Herruzo, Aleksandra Gruca, Ali Soleymani, Fanyou Wu, Yang Liu, Jingwei Xu, Jianjin Zhang, Jay Santokhi, Alabi Bojesomo, Hasan Al Marzouqi, Panos Liatsis, Pak Hay Kwok, Qi Qi, and Sepp Hochreiter

Dynamic channels

Dynamic traffic data: 8 channels encoding traffic volume and average speed per direction.

The IARAI Traffic4cast competition at NeurIPS 2019 showed that neural networks can successfully predict future traffic conditions 15 minutes into the future on simply aggregated GPS probe data in time and space bins, thus interpreting the challenge of forecasting traffic conditions as a movie completion task. U-nets proved to be the winning architecture then, demonstrating an ability to extract relevant features in the complex, real-world, geo-spatial process that is traffic derived from a large data set. The IARAI Traffic4cast challenge at NeurIPS 2020 build on the insights of the previous year and sought to both challenge some assumptions inherent in our 2019 competition design and explore how far this neural network technique can be pushed. We found that the prediction horizon can be extended successfully to 60 minutes into the future, that there is further evidence that traffic depends more on recent dynamics than on the additional static or dynamic location specific data provided and that a reasonable starting point when exploring a general aggregated geo-spatial process in time and space is a U-net architecture.

Proceedings of the NeurIPS 2020 Competition and Demonstration Track, PMLR, 133, 325-343, 2021.

View paper
IARAI Authors
Dr Michael Kopp​, Dr David Kreil, Dr Aleksandra Gruca, Henry Martin, Pedro Herruzo, Dr Sepp Hochreiter
Traffic and Navigation
Competition, Deep Learning, Machine Learning, Mobility, Traffic Prediction, Traffic4cast, U-Net


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