基于Spark的并行化出租车轨迹热点区域提取与分析

Extraction and Analysis of Hotspot Region of Parallel Taxi Trajectory Based on Spark

摘要: 从出租车GPS轨迹数据中可挖掘出丰富的居民出行规律信息，但数据量的不断增加，对数据挖掘的准确性和效率提出了新的要求。本文以成都市出租车GPS轨迹数据为研究对象，首先对原始数据进行失真数据剔除、多余字段删除和部分时段数据过滤三方面的预处理，其次进行地图匹配，最后利用Spark大数据处理平台，实现K-Means||算法，分为工作日和休息日的不同时段进行挖掘分析，得到成都市居民出行热点区域及其时空分布特征，并将单机K-Means算法和K-Means||算法的性能进行对比分析，结果表明：相比于单机，K-Means||算法在准确性和时间效率上具有优越性。

Abstract: The taxi GPS trajectory data can mine wealthy residents travel law information, but for the increasing number of data, there are new requirements have been put forward about the accuracy and efficiency of data mining. This paper takes Chengdu taxi GPS trajectory data as the research object. First, the distortion of the original data and the redundant field should be deleted, and partial time data should be filtered, then the map should be matched; finally using the spark Big Data processing platform, it realized K-means| |, divided into working days and rest days to analyze and get the hot spot area of Chengdu residents and its space-time distribution characteristics. Finally, com-pared the performance of the K-means and K-means| |, the result showed that K-means| | had superiority in accuracy and time efficiency compared with the single machine.