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Access to quality travel time information for roads in a road network hasbecome increasingly important with the rising demand for real-time travel timeestimation for paths within road networks. In the context of the Danish roadnetwork (DRN) dataset used in this paper, the data coverage is sparse andskewed towards arterial roads, with a coverage of 23.88% across 850,980 roadsegments, which makes travel time estimation difficult. Existing solutions forgraph-based data processing often neglect the size of the graph, which is anapparent problem for road networks with a large amount of connected roadsegments. To this end, we propose a framework for predicting road segmenttravel speed histograms for dataless edges, based on a latent representationgenerated by an adversarially regularized convolutional network. We apply apartitioning algorithm to divide the graph into dense subgraphs, and then traina model for each subgraph to predict speed histograms for the nodes. Theframework achieves an accuracy of 71.5% intersection and 78.5% correlation onpredicting travel speed histograms using the DRN dataset. Furthermore,experiments show that partitioning the dataset into clusters increases theperformance of the framework. Specifically, partitioning the road networkdataset into 100 clusters, with approximately 500 road segments in eachcluster, achieves a better performance than when using 10 and 20 clusters.

Partitioned Graph Convolution Using Adversarial and Regression Networks for Road Travel Speed Prediction