Learning to Transfer for Traffic Forecasting via Multi-task Learning

Understand

Deep neural networks have demonstrated superior performance in short-term traffic forecasting.

• However, most existing traffic forecasting systems assume that the training and testing data are drawn from the same underlying distribution, which limits their practical applicability.
• The NeurIPS 2021 Traffic4cast challenge is the first of its kind dedicated to benchmarking the robustness of traffic forecasting models towards domain shifts in space and time.
• This technical report describes our solution to this challenge.