Multitype Highway Mobility Analytics for Efficient Learning Model Design: A Case of Station Traffic Prediction

Abstract

The provincial highway transportation system supports substantial cross-city transitions of people and logistics, where the prediction tasks in terms of station/road traffic, urban transitions, and individual traveling are crucial for boosting data intelligence. However, to achieve efficient prediction model design, the predictability analytics with data is the basis, but has not been sufficiently investigated in the existing literature yet. To bridge this gap, in this paper, we study one large-scale dataset collected from one provincial highway transportation system, which contains totally 21,685,765 vehicles and 351,766,743 transaction records, and conduct a comprehensive mobility analytics on its predictable performance. We first investigate the station traffic by mining its spatio-temporal correlations, then examine the multi-type urban transition flows (i.e., people flows and logistics) by demystifying the difference and similarity between the two types of behaviors, and finally analyze the uncertainty of individual traveling behaviors in terms of the destination and arriving time. After that, in accordance with the analytical findings, we cast a case study of data-driven model design for station traffic prediction. Specifically, a novel learning model is devised, named STAR, i.e., Spatio-Temporal Attention based pRediction model, which consists of station outflow/inflow temporal embedding components and spatio-temporal attention blocks to push the limit of prediction capability. Extensive experiments corroborate the efficacy of the proposed STAR.