Challenge

The core and extended challenge in Traffic4cast 2021 aim at finding models that adapt to domain shifts in space and time. It’s an interesting observation to see that, also within the new transfer learning setting, UNet architecture based solutions still dominate the leaderboard when combined with a range of new and creative approaches utilizing ensembles, domain adaptation, patch-based subdivision and the underlying road graph.

The winning approaches show that the transfer across temporal as well as spatial domain shifts is possible. In the core competition which challenged participants to predict only the temporal shift, for instance, and with training data available for all cities, no city-specific models seemed to be needed. It’s therefore plausible that a single model could be trained to make predictions for all cities, evidently a tantalizing prospect for application scenarios predicting traffic on a global scale.

The solutions by the participants also help to better understand the limitations and open questions concerning the data and prediction tasks, such as how the road graph aspects could be formulated so that graph neural network approaches become competitive. The handling of sparse input data, for example, at night or in less dense areas, is another topical area of future research. This is also related to the more general problem of choosing an appropriate metric. Standard MSE often leads to insufficient modelling of sparse data, instead favoring predictions that regress to the mean. 

These questions are of interest to the wider academic community and, indeed, to anyone researching traffic predictions at scale. Hence, the Analysis and Interpretability Challenge addresses these questions as an interdisciplinary challenge on ML in Traffic Engineering. Specifically we invite colleagues world-wide to submit their latest research addressing any of the following key topical focus areas:

• Analysis and Interpretability of traffic predictions:

  • What does the model learn? E.g. rules vs statistics.

  • Comparison and analysis of competition winning models with other benchmarks or potentially new models.

  • How do the models deal with outliers and anomalies?

• Traffic representations and data

  • Understanding the data, e.g. temporal properties, correlations of volume and speed

  • Dealing with sparsity and unknown probe distributions

  • Representing and using enhanced road graphs

  • Visualizations for understanding traffic data and predictions

• Task formulation and metrics

  • Understanding the loss function

  • From generic MSE to distributional, traffic and/or physics aware metrics

  • Graph-based metrics

All submitted work will be peer-reviewed by the Scientific Committee of the workshop, and we encourage both quantitative as well as qualitative analyses. Specifically, prices are awarded by the Scientific Committee and are solely based on academic merit.