使用集成学习结合混合图神经网络和Transformer预测城市交通
Using ensemble learning with hybrid graph neural networks and transformers to predict traffic in cities
AI总结：
本文提出HybridST混合架构，集成图神经网络、多头时间Transformer和监督集成学习方法，在三个公共基准数据集上实验证明其在MAE和RMSE指标上优于经典基线，为智慧城市交通预测与规划提供了可扩展且易解释的解决方案。
摘要：
智能交通系统（ITS）仍然难以准确预测城市交通，特别是在具有复杂时空动态的大型多模态环境中。本文提出了HybridST，这是一种混合架构，集成了图神经网络（GNNs）、多头时间Transformer和监督集成学习方法（XGBoost或随机森林），以共同捕获空间依赖关系、长程时间模式以及包括天气、日历或控制状态在内的外源信号。我们在METR-LA、PEMS-BAY和Seattle Loop三个公共基准数据集上测试了我们的模型。这些数据集包含从高速公路传感器网络到车路协同感知的各种场景。实验结果表明，HybridST在MAE和RMSE等重要指标上始终优于经典基线（LSTM、GCN、DCRNN、PDFormer），同时仍具有高度的可扩展性和易解释性。所提出的框架为实时城市出行规划、能源优化和拥堵缓解策略提供了一条有前景的途径，特别是在智慧城市和2030年FIFA世界杯等重大事件的框架内。
英文摘要：
Intelligent transportation systems (ITS) still have a hard time accurately predicting traffic in cities, especially in big, multimodal settings with complicated spatiotemporal dynamics. This paper presents HybridST, a hybrid architecture that integrates Graph Neural Networks (GNNs), multi-head temporal Transformers, and supervised ensemble learning methods (XGBoost or Random Forest) to collectively capture spatial dependencies, long-range temporal patterns, and exogenous signals, including weather, calendar, or control states. We test our model on the METR-LA, PEMS-BAY, and Seattle Loop tree public benchmark datasets. These datasets include situations ranging from freeway sensor networks to vehicle-infrastructure cooperative perception. Experimental results show that HybridST consistently beats classical baselines (LSTM, GCN, DCRNN, PDFormer) on important metrics like MAE and RMSE, while still being very scalable and easy to understand. The proposed framework presents a promising avenue for real-time urban mobility planning, energy optimization, and congestion alleviation strategies, especially within the framework of smart cities and significant events such as the 2030 FIFA World Cup.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
