As global climate change intensifies, extreme rainfalls and floods become more frequent and pose a serious threat to urban sustainable development. Fast assessment of the rainfall disaster impact upon urban traffic and population plays an important role in improving disaster emergency management and incident response capabilities. This study adopts a time series anomaly detection method to discover and quantify the impact of rainfall-triggered flood on road traffic and human activities using real-time traffic condition information and mobile phone location request data. The anomaly detection method combines the STL time series decomposition technique and the extreme student deviation statistics to identify the response characteristics of traffic data and location requests during the event. The extreme rainfall event that occurred in Beijing on July 16, 2018 is used as a case study to examine the method effectiveness. The results show that the precipitation peaked in the morning and evening rush hours, during which the number of congested roads exceeded the average level by up to 150%. The anomaly detection analysis indicates that the number of congested roads and the traffic congestion index reached the outlier level. The anomaly analysis of human activity responses shows that the heavy rainfall event also caused an abnormal increase in the number of location requests, and the spatial distribution of the anomalous grids was highly correlated with the rainfall distribution one hour before. The above results not only prove the effectiveness of the big data and the anomaly detection method in understanding the impact of heavy rainfall events on urban traffic and population, but also provide new means for urban emergency response and management against rainfall disasters.