A Hybrid Processing System for Large-Scale Traffic Sensor Data

In recent years, with the further adoption of the Internet of Things and sensor technology, all kinds of intelligent transportation system (ITS) applications based on a wide range of traffic sensor data have had rapid development. Traffic sensor data gathered by large amounts of sensors show some ne...

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Bibliographic Details
Published in:IEEE access 2015, Vol.3, p.2341-2351
Main Authors: Zhao, Zhuofeng, Ding, Weilong, Wang, Jianwu, Han, Yanbo
Format: Article
Language:English
Subjects:
Batch processing
Computational modeling
Data models
Data processing
Digital media
Distributed databases
Hybrid systems
Intelligent transportation systems
Internet of Things
Parallel processing
real-time processing
Real-time systems
Sensors
spatio-temporal data object
Storms
stream computing
System implementation
Traffic sensor data
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Summary:In recent years, with the further adoption of the Internet of Things and sensor technology, all kinds of intelligent transportation system (ITS) applications based on a wide range of traffic sensor data have had rapid development. Traffic sensor data gathered by large amounts of sensors show some new features, such as massiveness, continuity, streaming, and spatio-temporality. ITS applications utilizing traffic sensor data can be divided into three main types: 1) offline processing of historical data; 2) online processing of streaming data; and 3) hybrid processing of both. Current research tends to solve these problems in separate solutions, such as stream computing and batch processing. In this paper, we propose a hybrid processing approach and present corresponding system implementation for both streaming and historical traffic sensor data, which combines spatio-temporal data partitioning, pipelined parallel processing, and stream computing techniques to support hybrid processing of traffic sensor data in real-time. Three types of real-world applications are explained in detail to show the usability and generality of our approach and system. Our experiments show that the system can achieve better performance than a popular open-source streaming system called Storm.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2015.2500258