地理空间大数据的发展使得交通拥堵问题的研究成为可能。特别是浮动车数据（FCD）非常适用于此类研究，因为FCD有助于预测交通拥堵瓶颈，并提供相应的解决方案以应对交通问题。以往研究探讨了降雨对道路速度的影响，但很少有研究关注降雨对超大城市中交通拥堵瓶颈的空间分布及变化的影响。本文通过整合多源数据（包括降雨数据、FCD和OpenStreetMap数据），结合PageRank算法与聚类算法，提出了一种指数计算与聚类（ICC）模型。研究区域选取中国华南地区最大的发达城市深圳。结果表明，市民出行存在三个高峰时段，分别为8:00-10:00、14:00-16:00和18:00-20:00。降雨后工作日道路速度下降6.20%，周末下降2.37%；交通拥堵区域分别增加23.53%和20.65%。此外，在深圳，降雨对工作日交通状况的影响显著大于周末。相较于传统的核密度分析方法，所提出的ICC模型能够更全面地揭示城市交通拥堵区域特征，有助于政策制定者优化缓解策略。

The development of geospatial big data makes it possible to study traffic-congestion issues. In particular, floating car data (FCD) is very suitable for it because FCD can help predict traffic-congestion bottlenecks and provide corresponding solutions to address traffic problems. Previous studies have discussed the impacts of rainfall on road speeds, but few studies have focused on the impacts of rainfall on the spatial distribution and changes in traffic-congestion bottlenecks throughout a mega-city. This article proposes an index calculation and clustering (ICC) model by integrating PageRank and clustering algorithms from multisource data, including rainfall data, FCD, and OpenStreetMap data. As the study area, we selected Shenzhen, which is the largest developed city in South China. The results demonstrate three peak periods of citizen travel, namely, 8:00-10:00, 14:00-16:00, and 18:00-20:00. Road speeds after rainfall decrease by 6.20% on weekdays and by 2.37% on weekends, and traffic-congestion areas increase by 23.53% and 20.65% on weekdays and on weekends, respectively. In addition, rainfall causes more significant effects on traffic conditions on weekdays compared with on weekends in Shenzhen. Compared with a traditional kernel density analysis, the proposed ICC model can offer a more thorough understanding of urban traffic-congestion areas, which can help policy makers optimize alleviation strategies.