Urban railways in coastal areas are exposed to the risk of extreme weather conditions. A cost-effective and robust wind monitoring system, as a vital part of the railway infrastructure, is essential for ensuring safety and efficiency. However, insufficient sensors along urban rail lines may result in failure to detect local strong winds, thus impacting urban rail safety and operational efficiency. This paper proposes a hybrid method based on historical wind speed data analysis to optimize wind monitoring system deployment. The proposed methodology integrates warning similarity and trend similarity with a linear combination and develops a constrained quadratic programming model to determine the combined weights. The methodology is demonstrated and verified based on a real-world case of an urban rail line. The results show that the proposed method outperforms the single similarity-based method and spatial interpolation approach in terms of both evaluation accuracy and robustness. This study provides a practical data-driven tool for urban rail operators to optimize their wind sensor networks with limited data and resources. It can contribute significantly to enhancing railway system operational efficiency and reducing the hazards on rail infrastructures and facilities under strong wind conditions. Additionally, the novel methodology and evaluation framework can be efficiently applied to the monitoring of other extreme weather conditions, further enhancing urban rail safety.

沿海地区的城市铁路面临极端天气条件的风险。经济高效且强大的风力监测系统作为铁路基础设施的重要组成部分，对于确保安全和效率至关重要。然而，城轨沿线传感器不足可能会导致无法检测到局部强风，从而影响城轨安全和运营效率。本文提出了一种基于历史风速数据分析的混合方法来优化风监测系统部署。所提出的方法将警告相似性和趋势相似性与线性组合相结合，并开发了约束二次规划模型来确定组合权重。该方法基于城市轨道交通的实际案例进行了论证和验证。结果表明，该方法在评估精度和鲁棒性方面均优于单一基于相似度的方法和空间插值方法。这项研究为城市轨道交通运营商提供了一种实用的数据驱动工具，以利用有限的数据和资源优化其风传感器网络。它可以为提高铁路系统运营效率、减少强风条件下铁路基础设施和设施的危害做出重大贡献。此外，新颖的方法和评估框架可以有效地应用于其他极端天气条件的监测，进一步提高城市轨道交通的安全性。这项研究为城市轨道交通运营商提供了一种实用的数据驱动工具，以利用有限的数据和资源优化其风传感器网络。它可以为提高铁路系统运营效率、减少强风条件下铁路基础设施和设施的危害做出重大贡献。此外，新颖的方法和评估框架可以有效地应用于其他极端天气条件的监测，进一步提高城市轨道交通的安全性。这项研究为城市轨道交通运营商提供了一种实用的数据驱动工具，以利用有限的数据和资源优化其风传感器网络。它可以为提高铁路系统运营效率、减少强风条件下铁路基础设施和设施的危害做出重大贡献。此外，新颖的方法和评估框架可以有效地应用于其他极端天气条件的监测，进一步提高城市轨道交通的安全性。