Spatial-Temporal Traffic Forecasting Based on Bottom-Up Representation Learning

To implement proactive traffic management, traffic forecasting becomes an essential function of modern intelligent transport systems (ITS). Traffic flows on motorways exhibit substantial variability, making it necessary to capture high-frequency patterns in the spatiotemporal model. To address the challenges, a representation learning approach is leveraged in this paper to extract high-level features that facilitate traffic forecasting on motorway. A bottom-up learning structure is proposed to sequentially extract information from local to the global level. Computational experiments show that simple models with informative representation may achieve satisfactory performance for traffic prediction.