Management Actions Enhanced Traffic Prediction Under Sparse Data

Accurate traffic prediction is crucial for real-time traffic management and a variety of subsequent applications. In the domain of traffic prediction, while the latest data-driven studies have achieved satisfactory results, they often overlook the significant problem of data sparsity (i.e., only part of the traffic system is observed) in real-world scenarios which can lead to insufficient data and erroneous predictions. Conventional strategies to counteract sparse data in traffic prediction problems typically involve a two-step process: initially imputing missing data, then predicting traffic state. A critical flaw in these existing methods is their assumption that traffic on adjacent roads equally influences unobserved roads, disregarding the impact of traffic management actions. In response to this challenge, we propose TMA-GNN, an approach that integrates traffic management actions into traditional data-driven models, treating traffic signal control as governing rules. This approach constrains the attention mechanism between roads, enabling real-time updates of road connectivity and effectively capturing diverse vehicle behavior patterns such as emerging from a residential area or entering a parking lot. These enhancements not only address the limitations of ignoring traffic strategies but also challenge the conventional assumption that vehicles always travel strictly downstream from upstream segments, thereby improving traffic state estimations on both observed and unobserved roads. Nonetheless, in certain extreme scenarios (e.g., when a major residential area is connected by a single road), the unobserved road may be unpredictable and lead to inaccurate imputations. To address this, TMA-GNN introduces a complementary strategy named EERA for evaluating the robustness of imputed values. This method effectively identifies roads with the highest uncertainty and prioritizes them for sensor installation, bridging data gaps on such unobserved roads. We conducted experiments on both synthetic and real-world datasets to validate the efficacy of the TMA-GNN framework and its components. Additionally, we carried out a series of case studies demonstrating the practical utility of EERA in real-world scenarios.