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The study focuses on mapping spatiotemporal patterns and detecting the potential drivers of traffic congestion with multi-source data. First, based on real-time traffic data retrieved from an online map, the k-means clustering algorithm was applied to classify the spatiotemporal distribution of congested roads. Then, we applied a geographical detector (Geo-detector) to mine the potential factors for each spatiotemporal pattern. The results showed six congestion patterns for intra-regional roads and inter-regional roads on weekdays. On both intra-regional and inter-regional roads, congestion density reflected by building height was the strongest indicator during the morning peak period. Public facilities such as hospitals, tourist sites and green spaces located near areas of employment or residential areas contributed to congestion during and off-peak hours. On intra-regional roads, the sparse road network and greater …