Parallel Algorithm for Road Network Kernel Density Estimation Based on Spark Computing Framework

Road network kernel density estimation is a cluster analysis method for event points under road network constraints. It is often used to study spatial distribution patterns of traffic accidents, urban crimes, vehicle trajectories and other events. The traditional serial algorithm of road network kernel density estimation has higher efficiency under the condition of small data volume, but with the increase of data volume, the performance of the algorithm is significantly reduced, which can not meet the actual application requirements. In this paper, an efficient parallel algorithm based on Spark computing framework is designed and implemented for road network segmentation and kernel density calculation in road network kernel density estimation method. Taking traffic accidents as an example, four groups of experiments are used for comparative analysis. The results show that the parallel algorithm of road network kernel density estimation based on Spark computing framework has high computational efficiency and good extensibility.