LoReST：面向大规模交通流预测的局部-区域时空网络
Learning Local Heterogeneity and Cross-Region Context for Large-Scale Traffic Forecasting
一篇交通预测论文，提出了 LoReST 网络，用局部加区域两级建模替代昂贵的全对交互，在 LargeST 四个数据集上误差明显下降。
LoReST 是一个面向大规模交通流预测的 Local-Region Spatial Temporal 网络，在节点邻域和路网区域两个粒度上建模空间依赖。它通过道路与方向特定的特征变换捕捉地理邻域内的异构依赖，并借助区域间注意力交换长程上下文以降低全对节点交互的计算成本。在 LargeST 基准的四个数据集上，LoReST 的 MAE、RMSE、MAPE 平均相对下降 4.78%、3.60% 和 5.75%。
Learning Local Heterogeneity and Cross-Region Context for Large-Scale Traffic Forecasting
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.
