Vehicle tracking data for thousands of urban vehicles and the availability of digital map provide urban planners unprecedented opportunities for better understanding urban transportation. In this paper, we aim to detect traffic hot spots on urban road networks using vehicle tracking data. Our approach first proposes an integrated map-matching algorithm based on the road buffer and vehicle driving direction, to find out which road segment the vehicle is travelling on. Then, we estimate travel speed by calculating the average the speed of every vehicle on a certain road segment, which indicates traffic status, and create the spatial weights matrices based on the connectivity of road segments, which expresses the spatial dependence between each road segment. Finally, the measure of global and local spatial autocorrelation is used to evaluate the spatial distribution of the traffic condition and reveal the traffic hot spots on the road networks. Experiments based on the taxi tracking data and urban road network data from Wuhan have been performed to validate the detection effectiveness.