ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2016, Vol. 53 ›› Issue (6): 1332-1341.doi: 10.7544/issn1000-1239.2016.20150458

• 软件技术 • 上一篇    下一篇

基于云架构的交通感知数据集成处理平台

赵卓峰,丁维龙,韩燕波   

  1. (北方工业大学计算机学院 北京 100144) (大规模流数据集成与分析技术北京市重点实验室(北方工业大学) 北京 100144) (edzhao@ncut.edu.cn)
  • 出版日期: 2016-06-01
  • 基金资助: 
    国家自然科学基金重点项目(61033006);北京市自然科学基金项目(4131001,4162021);北京市属高等学校创新团队建设项目(IDHT20130502);北方工业大学校科研基金项目

An Intergrated Processing Platform for Traffic Sensor Data Based on Cloud Architecture

Zhao Zhuofeng, Ding Weilong, Han Yanbo   

  1. (School of Computer Science and Technology, North China University of Technology, Beijing 100144) (Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data (North China University of Technology), Beijing 100144)
  • Online: 2016-06-01

摘要: 海量、多源、不间断的交通感知数据环境下,如何提供集成化的交通感知数据处理支持是多样化交通应用实施中的难点.现有的通用计算框架及平台由于缺少对具有时空相关等特征的交通感知数据和应用间交通感知数据共享的支持,使得交通感知数据处理应用的开发存在较高的复杂性并且易于造成大量重复的数据跨节点传输而影响应用性能.针对此问题,通过分析交通感知数据及其处理需求特征,提出一种基于可跨应用共享的时空数据对象的交通感知数据处理模型,通过引入时空数据对象这一新的概念抽象并提供易并行划分的时空数据对象组织及共享支持,实现分布计算中对时空型交通感知数据的优化管理.在此基础上,设计并实现了交通感知数据集成处理平台.通过实际应用和基于真实交通数据的实验测试表明:该平台相对于传统的交通感知数据处理方法及系统在性能及扩展性等方面均具有一定的优势.

关键词: 云架构, 交通感知数据, 时空数据对象, 实时MapReduce, 流计算

Abstract: With the continuous expansion of the scope of traffic sensor networks, traffic sensor data becomes widely available and is continuously being produced. Traffic sensor data gathered by large amounts of sensors shows the massive, continuous, streaming and spatio-temporal characteristics compared with traditional traffic data. How to provide intergrated support for multi-source, massive and continuous traffic sensor data processing is becoming one key issue of the implementation of diversified traffic applications. However, due to the absence of support for spatio-temporal traffic sensor data, it is difficult to develop corresponding applications and optimize the data transfer among different nodes in currenent distributed computing platforms. In this paper, we propose a traffic domain-specific processing model based on spatio-temporal data object. The spatio-temporal data object is treated as the first-class object in the distributed processing model. According to the model, we implement an intergrated processing platform for traffic sensor data based on the share-nothing architecture of cloud computing, which is designed to combine spatio-temporal data partition, pipelined parallel processing and stream computing to support traffic sensor data processing in a scalable architecture with real-time guarantee. Applications of the platform in real project and experiments based on real traffice sensor data show that our platform excels in performance and extensibility compared with traditional traffic sensor data processing system.

Key words: cloud architecture, traffic sensor data, spatio-temporal data object, real-time MapReduce, stream computing

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