引用本文: 郭宇达, 朱欣焰, 呙维, 佘冰. 基于Spark计算框架的路网核密度估计并行算法[J]. 武汉大学学报 ( 信息科学版), 2020, 45(2): 289-295.
GUO Yuda, ZHU Xinyan, GUO Wei, SHE Bing. Parallel Algorithm for Road Network Kernel Density Estimation Based on Spark Computing Framework[J]. Geomatics and Information Science of Wuhan University, 2020, 45(2): 289-295.
 Citation: GUO Yuda, ZHU Xinyan, GUO Wei, SHE Bing. Parallel Algorithm for Road Network Kernel Density Estimation Based on Spark Computing Framework[J]. Geomatics and Information Science of Wuhan University, 2020, 45(2): 289-295.

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

• 摘要: 路网核密度估计是路网约束下针对事件点的聚类分析方法，常用于研究交通事故、城市犯罪、车辆轨迹等事件的空间分布模式。传统单机串行的路网核密度估计算法在小数据量条件下的运行效率较高，但随着数据量的增加，算法性能显著下降，无法满足实际应用需求。针对路网核密度估计中的道路网分割和核密度计算，设计并实现了基于Spark计算框架的高效并行算法。以交通事故为例，通过4组实验进行对比分析。结果表明，基于Spark计算框架的路网核密度估计并行算法具有较高的运算效率，并具备良好的可拓展性。

Abstract: 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.

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