标题： GSMT：多公交轨迹预测的图融合与时空任务修正 显示英文标题

标题： GSMT: Graph Fusion and Spatiotemporal TaskCorrection for Multi-Bus Trajectory Prediction

摘要： 准确的公交车轨迹预测在智能交通系统中至关重要，尤其是在城市环境中。 在多模式数据获取受限的发展地区，尽管存在固有挑战，仅依赖车载GPS数据仍然是不可或缺的。 为了解决这个问题，我们提出了GSMT，一种混合模型，将图注意力网络（GAT）与序列到序列的循环神经网络（RNN）相结合，并引入了一个任务校正器，能够从大规模轨迹数据中提取复杂的的行为模式。 任务校正器通过聚类历史轨迹来识别不同的运动模式，并对GAT和RNN生成的预测进行微调。 具体来说，GSMT通过嵌入式混合网络融合动态公交车信息和静态站点信息来进行轨迹预测，并在初始预测生成后应用任务校正器进行二次优化。 这种两阶段方法使得在复杂条件下密集城市交通环境中运营的公交车能够进行多节点轨迹预测。 在马来西亚吉隆坡的真实数据集上进行的实验表明，我们的方法显著优于现有方法，在短期和长期轨迹预测任务中均表现出色。 显示英文摘要

摘要： Accurate trajectory prediction for buses is crucial in intelligent transportation systems, particularly within urban environments. In developing regions where access to multimodal data is limited, relying solely on onboard GPS data remains indispensable despite inherent challenges. To address this problem, we propose GSMT, a hybrid model that integrates a Graph Attention Network (GAT) with a sequence-to-sequence Recurrent Neural Network (RNN), and incorporates a task corrector capable of extracting complex behavioral patterns from large-scale trajectory data. The task corrector clusters historical trajectories to identify distinct motion patterns and fine-tunes the predictions generated by the GAT and RNN. Specifically, GSMT fuses dynamic bus information and static station information through embedded hybrid networks to perform trajectory prediction, and applies the task corrector for secondary refinement after the initial predictions are generated. This two-stage approach enables multi-node trajectory prediction among buses operating in dense urban traffic environments under complex conditions. Experiments conducted on a real-world dataset from Kuala Lumpur, Malaysia, demonstrate that our method significantly outperforms existing approaches, achieving superior performance in both short-term and long-term trajectory prediction tasks.