A core component of the Intelligent Transportation System (ITS) is road network, which forms the most basic transport infrastructure, and becomes widely applied in many traffic applications. In most traffic models, the spatial representation of road network is learned only through static graph connection while dynamic driver preference and traffic conditions in the real world are ignored. Therefore, in this paper, a novel trajectory-based road network representation is proposed. By mining vehicle trajectories, our proposed method can learn dynamic route choice through embeddings of each road in a next-hop prediction model. Then road correlations are calculated by the embeddings to build a latent correlation graph that can be applied in various traffic-related applications. Extensive experiment results prove the effectiveness and rationality of our proposed approach.

Traffic state prediction is essential in Intelligent Transportation Systems for surveillance, management, and daily commuting. For developing high-accuracy prediction models, real-world traffic state datasets are necessary for training model parameters and evaluating prediction results. However, limited by the existing traffic collection devices, most of the current open datasets for traffic state prediction cannot obtain accurate traffic flow information. In contrast, some datasets directly use detection devices in freeway systems, so they cannot reflect complex urban traffic states. Therefore, a dataset from advanced devices that can record the flow from point to point on an urban road network attracts more attention and drives the progress of research on traffic state prediction models. To deal with the above issues, we introduce a Suburban Traffic Flow dataset using Geomagnetic sensors, or STF-G dataset, constructed for traffic flow prediction. The STF-G dataset consists of 2.5 billion vehicle driving scenarios and 319 corresponding geomagnetic sensors. The data was collected over 20 months and processed with two regional road graphs. We also do the Benchmark experiments in STF-G for analyzing and evaluating the performance of graph neural network models in traffic flow prediction and compare them to the other datasets with the same baseline.