This paper addresses the challenge of traffic flow forecasting by proposing a novel Graph Neural Network (GNN)-based method called Spatio-Temporal Pivotal Graph Neural Networks (STPGNN). The authors identify a critical limitation in existing methods, which often overlook the importance of certain nodes (referred to as pivotal nodes) that exhibit extensive connections with multiple other nodes. These pivotal nodes pose a significant challenge due to their complex spatio-temporal dependencies. To tackle this issue, STPGNN introduces a Pivotal Node Identification Module (PIM) to identify these pivotal nodes based on their aggregation and distribution capabilities. Additionally, it proposes a Pivotal Graph Convolution Module (PGCM) to capture the intricate spatio-temporal dependencies around these pivotal nodes. The model also includes a parallel framework to extract spatio-temporal features on both pivotal and non-pivotal nodes. Extensive experiments on seven real-world traffic datasets demonstrate the effectiveness and efficiency of STPGNN compared to state-of-the-art baselines. The results show significant improvements in prediction accuracy, particularly on datasets like TAXI-BJ and PEMS08. The paper also includes a case study to validate the effectiveness of the pivotal nodes identified by the model and analyzes the impact of pivotal nodes on traffic flow predictions.This paper addresses the challenge of traffic flow forecasting by proposing a novel Graph Neural Network (GNN)-based method called Spatio-Temporal Pivotal Graph Neural Networks (STPGNN). The authors identify a critical limitation in existing methods, which often overlook the importance of certain nodes (referred to as pivotal nodes) that exhibit extensive connections with multiple other nodes. These pivotal nodes pose a significant challenge due to their complex spatio-temporal dependencies. To tackle this issue, STPGNN introduces a Pivotal Node Identification Module (PIM) to identify these pivotal nodes based on their aggregation and distribution capabilities. Additionally, it proposes a Pivotal Graph Convolution Module (PGCM) to capture the intricate spatio-temporal dependencies around these pivotal nodes. The model also includes a parallel framework to extract spatio-temporal features on both pivotal and non-pivotal nodes. Extensive experiments on seven real-world traffic datasets demonstrate the effectiveness and efficiency of STPGNN compared to state-of-the-art baselines. The results show significant improvements in prediction accuracy, particularly on datasets like TAXI-BJ and PEMS08. The paper also includes a case study to validate the effectiveness of the pivotal nodes identified by the model and analyzes the impact of pivotal nodes on traffic flow predictions.