地理空间大数据的发展使得通过浮动车数据（FCD）研究交通拥堵问题成为可能。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。然而，针对超大城市中交通拥堵瓶颈分布与变化的研究仍较为有限。本研究通过整合PageRank算法与聚类算法，结合多源数据（包括降雨数据、FCD及OpenStreetMap数据），提出一种指数计算与聚类（ICC）模型。选取中国华南地区最大发展城市深圳作为研究区域。结果表明，市民出行存在三个高峰时段：8:00-10:00、14:00-16:00和18:00-20:00。降雨后道路速度下降，交通拥堵区域增加。研究还定量分析了工作日与休息日交通拥堵的差异。所提出的ICC模型能够全面揭示城市交通拥堵区域特征，为政策制定者优化缓解策略提供支持。

The development of geospatial big data makes it possible to study traffic congestion issues through floating car data (FCD). FCD can help predict the traffic congestion bottlenecks and provide corresponding solutions to address traffic problems. However, few studies have focused on the distribution and changes in traffic congestion bottlenecks throughout a mega-city. This study proposes an index calculation and clustering (ICC) model by integrating PageRank and clustering algorithms via multisource data, including rainfall data, FCD and OpenStreetMap data. We selected Shenzhen, the largest developed city in South China, as the study area. The results demonstrate that there are three peak periods of citizen travel: 8:00-10:00, 14:00-16:00, and 18:00-20:00. Road speeds after rainfall decrease, and traffic congestion areas increase. The results also quantitatively analyzed the differences of traffic congestion between work day and rest day. The proposed ICC model can offer a thorough understanding of urban traffic congestion areas, which can help policy makers optimize alleviation strategies.