Leveraging Contextual Graphs for Stochastic Weight Completion in Sparse Road Networks

Road network applications, such as navigation, incident detection, and Point-of-Interest (POI) recommendation, make extensive use of network edge weights (e.g., traveling times). Some of these weights can be missing, especially in a road network where traffic data may not be available for every road. In this paper, we study the stochastic weight completion (SWC) problem, which computes the weight distributions of missing road edges. This is difficult, due to the intricate temporal and spatial correlations among neighboring edges. Moreover, the road network can be sparse, i.e., there is a lack of traveling information in a large portion of the network. To tackle these challenges, we propose the Contextual Graph Completion (ConGC). We propose to incorporate the contextual properties about the road network (e.g., speed limits, number of lanes, road types) to provide finer granularity of spatial correlations. Moreover, ConGC incorporates temporal and periodic dimensions of the road traffic. We evaluate ConGC against existing methods on three real road network datasets. They show that ConGC is more effective and efficient than state-of-the-art solutions.