发表机构
Northwestern Polytechnical University; City University of Hong Kong; Shenzhen Research Institute, City University of Hong Kong; Northwest University（西北工业大学; 香港城市大学; 香港城市大学深圳研究院; 西北大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出LoReST网络，通过关系感知局部聚合和跨区域注意力，高效建模大规模交通预测中的局部异质性与长距离上下文，在LargeST基准上显著降低误差。
AI 中文摘要
交通流预测对于智能交通系统至关重要。大规模交通预测需要联合建模局部空间依赖性和跨区域上下文。由于道路标识和行驶方向的差异，地理上相邻节点之间的空间依赖性具有异质性，而通过全对节点交互获取全局信息会带来巨大的计算成本。因此，在大规模交通预测中，捕获局部异质性同时高效获取长距离上下文仍然是一个重要挑战。为了解决这些挑战，我们提出了LoReST，一种局部区域空间时间网络，它在两个互补的粒度上建模空间依赖性：节点邻域和道路网络区域。具体来说，关系感知的局部聚合通过道路和方向特定的特征变换捕获地理邻域内的异质性依赖。跨区域交互通过平均池化构建区域表示，通过区域间注意力交换长距离上下文，并将其广播回节点。通过整合局部信息聚合与跨区域交互，LoReST能够有效地在大规模道路网络中实现空间依赖性学习。在LargeST基准的四个数据集上的实验显示，MAE、RMSE和MAPE分别平均相对降低了4.78%、3.60%和5.75%。
英文摘要
Traffic flow forecasting is essential to intelligent transportation systems. Large-scale traffic forecasting requires jointly modeling local spatial dependencies and cross-region context.Spatial dependencies between geographically neighboring nodes are heterogeneous due to differences in road identity and travel direction, while acquiring global information through allpairs node interactions incurs substantial computational costs. Therefore, capturing local heterogeneity while efficiently acquiring long-range context remains an important challenge in largescale traffic forecasting. To address these challenges, we propose LoReST, a Local-Region Spatial Temporal network that models spatial dependencies at two complementary granularities: node neighborhoods and road network regions. Specifically, relation-aware local aggregation captures heterogeneous dependencies within geographic neighborhoods through road and direction specific feature transformations. Cross-region interaction constructs region representations through mean pooling, exchanges long range context via inter-region attention, and broadcasts it back to nodes. By integrating local information aggregation with crossregion interaction, LoReST is able to effectively achieve spatial dependency learning in large-scale road networks. Experiments on four datasets of the LargeST benchmark show average relative reductions of 4.78%, 3.60%, and 5.75% in MAE, RMSE, and MAPE, respectively.
