A Hybrid Processing System for Large-Scale Traffic Sensor Data

作者：Zhao Zhuofeng^*; Ding Weilong; Wang Jianwu; Han Yanbo

来源：IEEE Access, 2015, 3: 2341-2351.

DOI：10.1109/ACCESS.2015.2500258

• Traffic sensor data
• spatio-temporal data object
• real-time processing
• stream computing

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.

• 出版日期2015
• 单位北方工业大学