为提升城市洪涝风险评估结果的可靠性，以杭州市未来科技城为研究区，利用MIKE模型开展了1、10、50 a重现期下的内涝情景模拟；在传统静态暴露性指标基础上，引入分时段交通拥堵数据，结合层次分析法（AHP）构建了基于危险性-暴露性-脆弱性-防灾减灾能力（H-E-V-R）共4个维度8个指标的洪涝综合风险评估体系。研究结果表明：①验证结果显示，构建的基于MIKE FLOOD的一维地下管网与二维地表耦合水动力模型在内涝点淹没水深的模拟误差控制在5%以内，且综合径流系数的计算误差均<15%，模型模拟精度合格，能较好地反映研究区洪水演进特征；②随着设计暴雨重现期增大，淹没水深和淹没时间均增大，内涝危险性也显著增大，综合洪涝风险也整体增大；③研究区不同代表时段的交通暴露度存在差异，早高峰时段交通暴露度极值更高，而晚高峰高暴露度路段的空间分布更为广泛；④50 a重现期设计暴雨下，研究区南部区域风险整体较低，万景村委会、顾家桥社区仍未出现高风险区，而中北部社区中高风险区大多>22%，塘河社区甚至出现2%的极高风险区。引入分时段交通拥堵特征有效识别了高暴露度路段；在“H-E-V-R”框架下计算得到的综合风险呈现“中北部高、南部低”的空间差异，高风险区聚集于中北部城镇化核心地带。研究结果可为未来社区级别的城市防洪排涝工作以及局部路段的洪涝灾害交通管控工作提供参考。

[Objective] Traditional flood risk assessment frameworks predominantly rely on static exposure indicators，such as population density and land use types，overlooking the tidal movement of people and vehicles between residential，working，and transportation areas. This limitation compromises the spatiotemporal accuracy of risk assessments. To address this，we integrate time-dependent traffic congestion data into the conventional urban flood risk assessment framework，aiming to expand the dimensionality of exposure characterization and provide a supplementary perspective for urban flood risk evaluation. [Methods] Taking the low-lying Future Sci-Tech City in Hangzhou as a case study，we developed a coupled 1D-2D hydrodynamic model （MIKE FLOOD） to simulate waterlogging scenarios under 1-，10-，and 50-year return periods. Building upon traditional static indicators，time-dependent traffic congestion data were introduced to construct a comprehensive flood risk assessment system based on the Hazard-Exposure-Vulnerability-Resilience （H-E-V-R） framework comprising eight indicators： maximum inundation depth，inundation duration，population density，traffic exposure，building density，greenland coverage，distribution of critical assets，and emergency rescue capacity （measured by euclidean distance）. The model was validated using observed inundation depths from actual rainfall events. [Results] Model validation demonstrated high accuracy，with simulation errors for inundation depth at waterlogging points controlled within 5%，and comprehensive runoff coefficient errors of 13.7% and 13.9%，respectively. The model effectively captured the flood evolution characteristics of the study area. As the design rainfall return period increased，both inundation depth and duration escalated，leading to a significant rise in overall flood hazard and comprehensive risk. Traffic exposure varied across different time periods. The morning peak exhibited higher maximum exposure values （0.693），whereas the evening peak showed a more spatially extensive distribution of highly exposed road segments. Under the 50-year return period scenario，the southern part of the study area remained at relatively low risk. In contrast，medium-to-high risk areas in the central and northern communities generally exceeded 22%，with Tanghe Community even experiencing a 2% area of extremely high risk. [Conclusion] The incorporation of time-dependent traffic congestion characteristics effectively identifies road segments with high exposure. Under the H-E-V-R framework，the comprehensive risk exhibits a distinct spatial pattern of “higher in the central-northern regions and lower in the south”，with high-risk zones concentrated in the urbanized core of the central-northern area. These findings provide a valuable reference for community-level urban flood control and traffic management during flood events.