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Its current situation and future trends are valuable for vehicles guidance and transportation security management. As a domain routine analysis, daily detection of traffic hotspots faces challenges in efficiency and precision, because huge data deteriorates processing latency and many correlative factors cannot be fully considered. In this paper, an ensemble-learning based method for potential traffic hotspots detection is proposed. Considering time, space, meteorology, and calendar conditions, daily traffic volume is modeled on heterogeneous data, and trends predictive error can be reduced through gradient boosting regression technology. 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