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.