Spatiotemporal time series forecasting plays a key role in a wide range of real-world applications. While significant progress has been made in this area, fully capturing and leveraging spatiotemporal heterogeneity remains a fundamental challenge. Therefore, we propose a novel Heterogeneity-Informed Meta-Parameter Learning scheme. Specifically, our approach implicitly captures spatiotemporal heterogeneity through learning spatial and temporal embeddings, which can be viewed as a clustering process. Then, a novel spatiotemporal meta-parameter learning paradigm is proposed to learn spatiotemporal-specific parameters from meta-parameter pools, which is informed by the captured heterogeneity. Based on these ideas, we develop a Heterogeneity-Informed Spatiotemporal Meta-Network (HimNet) for spatiotemporal time series forecasting. Extensive experiments on five widely-used benchmarks demonstrate our method achieves state-of-the-art performance while exhibiting superior interpretability. Our code is available at https://github.com/XDZhelheim/HimNet.

With the rapid development of the Intelligent Transportation System (ITS), accurate traffic forecasting has emerged as a critical challenge. The key bottleneck lies in capturing the intricate spatio-temporal traffic patterns. In recent years, numerous neural networks with complicated architectures have been proposed to address this issue. However, the advancements in network architectures have encountered diminishing performance gains. In this study, we present a novel component called spatio-temporal adaptive embedding that can yield outstanding results with vanilla transformers. Our proposed Spatio-Temporal Adaptive Embedding transformer (STAEformer) achieves state-of-the-art performance on five real-world traffic forecasting datasets. Further experiments demonstrate that spatio-temporal adaptive embedding plays a crucial role in traffic forecasting by effectively capturing intrinsic spatio-temporal relations and chronological information in traffic time series.

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.