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shanghaishanghai
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导语:在智慧城市建设中,精确的交通流量预测是实现高效城市规划和交通管理的关键。近日,香港大学发布了一项名为“OpenCity”的研究,旨在开发一个多功能、强鲁棒性和高适应性的时空基础模型,用于交通流量的预测。

研究背景:
现有的交通预测模型在面对未知区域和城市的零样本预测任务,以及长期预测时,表现往往不尽如人意。这些问题主要归因于交通数据在空间和时间上的异质性,以及跨时间和空间的显著分布变化。

研究目标:
开发一个多功能、强鲁棒性和高适应性的时空基础模型,用于交通流量的预测。

研究方法:
为此,研究团队设计了一种新型的基础模型——OpenCity。该模型融合了Transformer和图神经网络,以模拟交通数据中的复杂时空依赖性。通过在大规模、多样化的交通数据集上进行预训练,OpenCity能够学习到丰富且具有泛化能力的特征表示,这些特征表示适用于多种交通预测场景。

实验结果:
实验结果表明,OpenCity在零样本预测方面表现出色,且具有良好的可扩展性。在多个数据集上,OpenCity能够保持在前两名,与最佳性能(MAE)的差距也控制在8%以内。此外,OpenCity在所有测试类别中均提供了高质量的结果,展现了其出色的稳定性和多功能性。

研究意义:
OpenCity的成功开发有望为智慧城市建设提供强有力的技术支持,助力城市规划者和交通管理者制定有效的长期策略,优化资源分配,改善出行体验。

代码链接:https://github.com/HKUDS/OpenCity
论文链接:http://arxiv.org/abs/2408.10269
实验室主页:https://sites.google.com/view/chaoh01


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