针对高铁站周边路网交通流量大、客流集散需求高、道路网络单元耦合作用显著的特性，在网络拓扑结构的基础上，提出考虑交通需求动态变化和网络单元耦合作用的高铁站周边路网风险辨识模型。以西安北站周边路网为例，研究了不同交通需求下高铁站周边路网风险节点的动态变化，分析了周边网络中高风险节点失效造成的影响;并与未考虑网络单元耦合作用的风险辨识模型进行了比较，验证了所提出模型的优越性。研究结果表明，不同交通需求下，高铁站周边路网风险节点的空间分布具有差异性，距高铁站1km范围内耦合作用强的路段和交叉口风险变化更显著;当交通需求增大到原来的3倍后，节点和路段的风险显著增加，风险变化率最高达到20％;当交通需求增大到原来的5倍后，节点和路段风险变化率最高达到42％;网络单元失效后用户平均出行时间损失最高可达2.7h。考虑网络单元耦合作用的风险辨识模型可有效识别动态交通需求下网络风险的动态变化，从而保障路网的可靠运行。

According to the characteristics of large traffic flow, high demand for passenger distribution and significant coupling of road network units around high-speed railway stations, this paper proposed a risk identification model of road network around high-speed railway station considering the dynamic change of traffic demand and the coupling of network units on the basis of network topology. Taking the road network around Xi′an North Railway Station as an example, the dynamic changes of risk nodes in the road network around the high-speed railway station under different traffic demand were studied. The influence caused by the failure of high-risk nodes in the surrounding network was analyzed. At the same time, it was compared with the risk identification model without considering the coupling effect of network elements, which verified the superiority of the model proposed. The results showed that under different traffic demand, the spatial distribution of risk nodes in the road network around the high-speed railway station was different, the risk of sections and intersections within 1km away from the high-speed railway station changed significantly; when the traffic demand increased to three times of the original, the risk of nodes and sections increased significantly, and the highest risk change rate was 20%; when the traffic demand increased to five times of the original, the maximum risk change rate of nodes and sections reached 42%; after the network unit fails, the users′ average travel time loss could reach up to 2.7 hours. The risk identification model considering the coupling effect of network units can effectively identify the dynamic changes of network risk under dynamic traffic demand, and ensure the reliable operation of road network.