Journal of Computer Science and Technology ›› 2020, Vol. 35 ›› Issue (5): 1084-1098.doi: 10.1007/s11390-020-9724-x

Special Issue: Computer Networks and Distributed Computing

• Computer Networks and Distributed Computing • Previous Articles     Next Articles

A Spatiotemporal Causality Based Governance Framework for Noisy Urban Sensory Data

Bi-Ying Yan1,2, Member, CCF, Chao Yang3,4, Senior Member, CCF, Member, ACM, IEEE, Pan Deng5,*, Senior Member, CCF, Member, ACM, Qiao Sun1, Feng Chen1,6, and Yang Yu1,2,7        

  1. 1 Institute of Software, Chinese Academy of Sciences, Beijing 100190, China;
    2 University of Chinese Academy of Sciences, Beijing 100049, China;
    3 School of Mathematical Sciences, Peking University, Beijing 100871, China;
    4 Peng Cheng Laboratory, Shenzhen 518052, China;
    5 Research Institute for Frontier Science, Beihang University, Beijing 100191, China;
    6 Guiyang Academy of Information Technology, Guiyang 550081, China;
    7 Guiyang Municipal Commission of Transport, Guiyang 550003, China
  • Received:2019-05-18 Revised:2019-12-09 Online:2020-09-20 Published:2020-09-29
  • Contact: Pan Deng E-mail:pandeng@buaa.edu.cn
  • Supported by:
    This work was supported in part by the National Key Research and Development Program of China under Grant No. 2018YFC0-831500.

Urban sensing is one of the fundamental building blocks of urban computing. It uses various types of sensors deployed in different geospatial locations to continuously and cooperatively monitor the natural and cultural environment in urban areas. Nevertheless, issues such as uneven distribution, low sampling rate and high failure ratio of sensors often make their readings less reliable. This paper provides an innovative framework to detect the noise data as well as to repair them from a spatial-temporal causality perspective rather than to deal with them individually. This can be achieved by connecting data through monitored objects, using the Skip-gram model to estimate spatial correlation and long shortterm memory to estimate temporal correlation. The framework consists of three major modules: 1) a space embedded Bidirectional Long Short-Term Memory (BiLSTM)-based sequence labeling module to detect the noise data and the latent missing data; 2) a space embedded BiLSTM-based sequence predicting module calculating the value of the missing data; 3) an object characteristics fusion repairing module to correct the spatial and temporal dislocation sensory data. The approach is evaluated with real-world data collected by over 3 000 electronic traffic bayonet devices in a citywide scale of a medium-sized city in China, and the result is superior to those of several referenced approaches. With a 12.9% improvement in data accuracy over the raw data, the proposed framework plays a significant role in various real-world use cases in urban governance, such as criminal investigation, traffic violation monitoring, and equipment maintenance.

Key words: trajectory data; recurrent neural network; spatiotemporal (ST) big data; urban computing;

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