ZTE

ZTE Communications ›› 2013, Vol. 11 ›› Issue (2): 55-62.DOI: DOI:10.3969/j.issn.1673-5188.2013.02.009

• Research Paper • Previous Articles    

A System for Detecting Refueling Behavior along Freight Trajectories and Recommending Refueling Alternatives

Ye Li1, Fan Zhang1, Bo Gan1, and Chengzhong Xu1,2   

  1. 1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
    2. Department of Electrical and Computer Engineering, Wayne State University, MI 48202, USA
  • Received:2013-05-18 Online:2013-06-25 Published:2013-06-25
  • About author:Ye Li (li.ye@siat.ac.cn) is a research assistant at Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences. His research interests include big-data analysis and spatial data mining. He received his BS degree in geographic information science from South China Normal University in 2012.

    Fan Zhang (zhangfan@siat.ac.cn) is an associate professor at the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences. Dr. Zhang’s research interests include cloud computing, big-data analysis, data security, and privacy protection. He receicved his BS, MS, and PhD degrees in electronic and information engineering from Huazhong University of Science and Technology in 2002, 2004 and 2007.

    Bo Gan (bo.gan@siat.ac.cn) is a visiting research student at Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences. His research interests include big-data analysis. He is studying his MS degree in communication and information systems at Wuhan University of Technology.

    Chengzhong Xu (cz.xu@siat.ac.cn) is a professor of electrical and computer engineering at Wayne State University. He is director of the Cloud and Internet Computing Laboratory (CIC), Wayne State University, and director of the Cloud Research Computing Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences. Dr. Xu's research interests include resource management for improving the performance, reliability, availability, power efficiency, and security of networked computing systems. The systems of particular interest to Dr. Xu are distributed systems and the internet, servers and datacenters, scalable parallel computers, and wireless embedded devices. He is an editor of a number of journals, including IEEE Trans. on Parallel and Distributed Systems (TPDS) andJournal of Parallel and Distributed Computing (JPDC) . Dr. Xu obtained his BSc and MSc degrees in computer science and engineering from Nanjing University in 1986 and 1989. He received his Ph.D degree in computer science and engineering from the University of Hong Kong in 1993.

A System for Detecting Refueling Behavior along Freight Trajectories and Recommending Refueling Alternatives

Ye Li1, Fan Zhang1, Bo Gan1, and Chengzhong Xu1,2   

  1. 1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
    2. Department of Electrical and Computer Engineering, Wayne State University, MI 48202, USA
  • 作者简介:Ye Li (li.ye@siat.ac.cn) is a research assistant at Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences. His research interests include big-data analysis and spatial data mining. He received his BS degree in geographic information science from South China Normal University in 2012.

    Fan Zhang (zhangfan@siat.ac.cn) is an associate professor at the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences. Dr. Zhang’s research interests include cloud computing, big-data analysis, data security, and privacy protection. He receicved his BS, MS, and PhD degrees in electronic and information engineering from Huazhong University of Science and Technology in 2002, 2004 and 2007.

    Bo Gan (bo.gan@siat.ac.cn) is a visiting research student at Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences. His research interests include big-data analysis. He is studying his MS degree in communication and information systems at Wuhan University of Technology.

    Chengzhong Xu (cz.xu@siat.ac.cn) is a professor of electrical and computer engineering at Wayne State University. He is director of the Cloud and Internet Computing Laboratory (CIC), Wayne State University, and director of the Cloud Research Computing Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences. Dr. Xu's research interests include resource management for improving the performance, reliability, availability, power efficiency, and security of networked computing systems. The systems of particular interest to Dr. Xu are distributed systems and the internet, servers and datacenters, scalable parallel computers, and wireless embedded devices. He is an editor of a number of journals, including IEEE Trans. on Parallel and Distributed Systems (TPDS) andJournal of Parallel and Distributed Computing (JPDC) . Dr. Xu obtained his BSc and MSc degrees in computer science and engineering from Nanjing University in 1986 and 1989. He received his Ph.D degree in computer science and engineering from the University of Hong Kong in 1993.

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Abstract: Smart refueling can reduce costs and lower the possibility of an emergency. Refueling intelligence can only be obtained by mining historical refueling behaviors from big data; however, without devices, such as fuel tank cursors, and cooperation from drivers, these behaviors are hard to detect. Thus, detecting refueling behaviors from big data derived from easy-to-approach trajectories is one of the most efficient retrieve evidences for research of refueling behaviors. In this paper, we describe a complete procedure for detecting refueling behavior in big data derived from freight trajectories. This procedure involves the integration of spatial data mining and machine-learning techniques. The key part of the methodology is a pattern detector that extends the naive Bayes classifier. By drawing on the spatial and temporal characteristics of freight trajectories, refueling behaviors can be identified with high accuracy. Further, we present a refueling prediction and recommendation system to show how our refueling detector can be used practically in big data. Our experiments on real trajectories show that our refueling detector is accurate, and the system performs well.

Key words: spatial data mining, trajectory processing, big data

摘要： Smart refueling can reduce costs and lower the possibility of an emergency. Refueling intelligence can only be obtained by mining historical refueling behaviors from big data; however, without devices, such as fuel tank cursors, and cooperation from drivers, these behaviors are hard to detect. Thus, detecting refueling behaviors from big data derived from easy-to-approach trajectories is one of the most efficient retrieve evidences for research of refueling behaviors. In this paper, we describe a complete procedure for detecting refueling behavior in big data derived from freight trajectories. This procedure involves the integration of spatial data mining and machine-learning techniques. The key part of the methodology is a pattern detector that extends the naive Bayes classifier. By drawing on the spatial and temporal characteristics of freight trajectories, refueling behaviors can be identified with high accuracy. Further, we present a refueling prediction and recommendation system to show how our refueling detector can be used practically in big data. Our experiments on real trajectories show that our refueling detector is accurate, and the system performs well.

关键词: spatial data mining, trajectory processing, big data