Using cloud computing to process intensive floating car data for urban traffic surveillance

被引:73
作者
Li, Q. [1 ,2 ]
Zhang, T. [1 ,2 ]
Yu, Y. [3 ]
机构
[1] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430072, Peoples R China
[2] Minist Educ China, Engn Res Ctr Spatiotemporal Data Smart Acquisit &, Wuhan, Peoples R China
[3] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430072, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
floating car data; urban traffic systems; cloud computing; Bigtable; MapReduce; GEOSPATIAL CYBERINFRASTRUCTURE; ALGORITHMS; FUTURE;
D O I
10.1080/13658816.2011.577746
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The advances in data collection techniques and geosimulation models have contributed to the abundance of geospatial data in urban systems. The surveillance of urban traffic systems relies on the effective handling of near real-time traffic observation data, which is usually data-intensive in nature. We investigated the processing of massive floating car data (FCD) for traffic surveillance in cloud-computing environments, with the goal of exploring the use of emerging cloud-computing technologies to solve data-intensive geospatial problems in urban traffic systems. The experimental results indicated that cloud-computing technologies such as Bigtable and MapReduce can provide substantial utility for data-intensive geospatial computing, as both scalability and near real-time computational performance can be adequately enhanced through our proposed data storage, management, and parallel processing models. The applicability and utility of cloud computing was evaluated for three typical geospatial computing tasks for urban traffic monitoring, namely, FCD query, FCD map matching, and speed computation for road links.
引用
收藏
页码:1303 / 1322
页数:20
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