The surprising efficiency of framing geo-spatial time series forecasting as a video prediction task - Insights from the IARAI Traffic4cast Competition at NeurIPS 2019

Deep Neural Networks models are state-of-the-art solutions in accurately forecasting future video frames in a movie. A successful video prediction model needs to extract and encode semantic features that describe the complex spatio-temporal correlations within image sequences of the real world. The IARAI Traffic4cast Challenge of the NeurIPS Competition Track 2019 for the first time introduced the novel argument that this is also highly relevant for urban traffic. By framing traffic prediction as a movie completion task, the challenge requires models to take advantage of complex geo-spatial and temporal patterns of the underlying process. We here report on the success and insights obtained in a first Traffic Map Movie forecasting challenge. Although short-term traffic prediction is considered hard, this novel approach allowed several research groups to successfully predict future traffic states in a purely data-driven manner from pixel space. We here expand on the original ratio∗ Work performed at SEAT, S.A. c © 2020 D.P. Kreil et al. Efficiency of framing geo-spatial time series forecasting as a video prediction task nale, summarize key findings, and discuss promising future directions of the Traffic4cast competition at NeurIPS.