[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.