Learning Local Heterogeneity and Cross-Region Context for Large-Scale Traffic Forecasting

2026-09-23

AI Summary

为解决大规模交通流量预测中局部异质性和跨区域上下文获取问题，提出LoReST模型，通过节点邻域和道路网络区域两种粒度建模空间依赖性。

Abstract

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.

Problem

• Traffic Forecasting
• Spatial Dependencies
• Cross-Region Context
• Local Heterogeneity
• Large-Scale Road Networks

Innovation

• relation-aware local aggregation
• cross-region interaction
• spatial dependency learning