Taxi Trajectory Data Analysis Method Based on Regional Density Division

LI Jian-bin; CHEN Zhong-hui; SU Zhi-peng

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Taxi Trajectory Data Analysis Method Based on Regional Density Division

更新时间：2026-09-04

- Taxi Trajectory Data Analysis Method Based on Regional Density Division
- Vol. 36, Issue 2, Pages: 53-56(2019)
- 作者机构：
  
  福州大学物理与信息工程学院,福建,福州,350108
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2019
- 稿件说明：
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李剑斌, 陈忠辉, 苏志鹏. 基于区域密度划分的车辆轨迹数据分析方法研究[J]. 微电子学与计算机, 2019, 36(2): 53-56.

LI Jian-bin, CHEN Zhong-hui, SU Zhi-peng. Taxi Trajectory Data Analysis Method Based on Regional Density Division[J]. 2019, 36(2): 53-56.
李剑斌, 陈忠辉, 苏志鹏. 基于区域密度划分的车辆轨迹数据分析方法研究[J]. 微电子学与计算机, 2019, 36(2): 53-56. DOI：

LI Jian-bin, CHEN Zhong-hui, SU Zhi-peng. Taxi Trajectory Data Analysis Method Based on Regional Density Division[J]. 2019, 36(2): 53-56. DOI：

摘要

车联网是目前世界交通领域的一个热点.车联网可以通过实时处理和挖掘这些数据中的新信息而变得更加智能化.随着车联网的发展

从设备监控中采集到的海量数据需要更高的数据存储和查询性能.本文将Apache Spark作为基于HDFS高可用集群的统一集群计算平台

提出并设计了一种基于Spark的车辆轨迹数据分析方法

通过区域划分对海量数据进行了挖掘

解决道路堵塞问题

对辅助城市路径规划有一定的研究意义.

Abstract

Vehicle networking is a hot spot in the world transportation industry. Vehicle networking can become more intelligent by processing and mining new information in these data in real time. With the development of Vehicle networking

the massive data collected from device monitoring requires higher data storage and query performance. This paper uses Apache Spark as a unified cluster computing platform based on HDFS high-availability clusters

proposes and designs a Spark-based vehicle trajectory data analysis method

and uses area division to mine large amounts of data to solve road congestion problems and to assist urban roads. Planning has certain research significance.

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references

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孙川, 吴超仲, 褚端峰, 等.基于车联网数据挖掘的营运车辆驾驶速度行为聚类研究[J].交通运输系统工程与信息, 2015, 15(6):82-87. DOI: 82

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郭伟, 高媛媛, 刘鑫焱.改进的基于混合高斯模型的运动目标检测算法[J].计算机工程与应用, 2016, 52(13):195-200. DOI: 195

Anant R, Sunita J, Jalal A S, et al. A Density Based Algorithm for Discovering Density Varied Clusters in Large Spatial Databases[J]. International Journal of Computer Applications, 2011, 3(6):1-4. DOI: 1

田保慧, 郭彬.基于时空特征分析的短时交通流预测模型[J].重庆交通大学学报(自然科学版), 2016, 35(3):105-109. DOI: 105

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