面向复杂交通环境的气象敏感性分析：以吐和高速新和段为例

Meteorological Sensitivity Analysis for Complex Traffic Environments: A Case Study of the Xinhe Section of the Tu-He Expressway

摘要: 本文聚焦于地形地貌复杂的G3012吐和高速新和段(K643~K753)，旨在定量解析复杂交通环境下交通事故对气象要素的敏感性特征及其致灾机理。研究利用2020年至2024年的交通事故记录与高时空分辨率的同步气象观测资料，综合运用Pearson相关性分析、PCA-K-Means聚类及特殊天气归因统计等方法，深度解构了气象环境对交通安全的驱动机制。研究发现，该区域交通事故呈现显著的“夏冬双峰”季节性波动及“午后疲劳、晚间通勤”的日内双峰潮汐特征，且不同路段对气象因子的敏感性存在显著的空间异质性。其中，吐和上行K707公里处表现为典型的夏季干热大风敏感型，事故频数与高温及极大风速呈显著正相关，主要受强侧风与高温疲劳耦合驱动；相比之下，K675公里处呈现典型的冬季冰雪凝冻敏感型特征，与低温及低能见度高度相关，反映了黑冰与霜雾环境下的极高致灾性。通过聚类分析，研究进一步精准识别出“冬季低温高湿”、“夏季高温大风”及“常态气候背景”三种关键致灾模式，清晰展示了风险在特征空间中的两极化分离态。基于上述气象敏感性分异规律，本文提出了“一路一策”的分级分类管控建议，通过实施差异化的监测预警与养护策略，为提升复杂地貌下高速公路交通气象防灾减灾能力提供了科学依据与决策参考。

Abstract: This study focuses on the Xinhe section (K643~K753) of the G3012 Tuhe Expressway, characterized by its complex terrain, aiming to quantitatively analyze the sensitivity of traffic accidents to meteorological factors and their underlying hazard mechanisms. Utilizing traffic accident records from 2020 to 2024 alongside high-spatiotemporal-resolution concurrent meteorological observations, the study employs Pearson correlation analysis, PCA-K-Means clustering, and specialized weather attribution statistics to deeply deconstruct the driving mechanisms of the meteorological environment on traffic safety. The results indicate that traffic accidents in this region exhibit a pronounced seasonal pattern with “double peaks in summer and winter” and daily variations characterized by “afternoon fatigue and evening commuting”. Furthermore, significant spatial heterogeneity exists regarding the sensitivity of different road sections to meteorological factors. Specifically, the K707 km mark on the Tuhe uphill section is identified as a typical summer dry-hot and strong-wind sensitive type, where accident frequency is significantly positively correlated with high temperatures and maximum wind speeds, primarily driven by the coupling of strong crosswinds and high-temperature-induced fatigue. In contrast, the K675 km mark exhibits a typical winter ice-snow freezing sensitive type, showing a high correlation with low temperatures and reduced visibility, reflecting the extreme hazard potential of black ice and frost-fog conditions. Through clustering analysis, the study precisely identifies three key hazard patterns: “winter low-temperature high-humidity,” “summer high-temperature strong-wind,” and a “normal climatic background,” clearly demonstrating a bipolar separation of risks within the feature space. Based on these divergent meteorological sensitivity patterns, this paper proposes “road-specific and situation-specific” hierarchical management strategies. By implementing differentiated monitoring, early warning, and maintenance protocols, this research provides a scientific basis and decision-making reference for enhancing traffic meteorological disaster prevention and mitigation capabilities in complex terrains.