MOIR/MT: monitoring large-scale road network traffic in real-time

  • Authors:

  • Kuien Liu;Ke Deng;Zhiming Ding;Mingshu Li;Xiaofang Zhou

  • Affiliations:

  • Chinese Academy of Sciences, China;The University of Queensland, Brisbane, Australia;Chinese Academy of Sciences, China;Chinese Academy of Sciences, China;The University of Queensland, Brisbane, Australia

  • Venue:
  • Proceedings of the VLDB Endowment
  • Year:
  • 2009

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Abstract

Floating Car Data (FCD) provides an economic complement to infrastructure-based traffic monitoring systems. Based on our previous MOIR platform [5], we use FCD as the data source for large-scale real-time traffic monitoring. This new function brings a challenge of efficiently handling of streaming data from a very large number of moving objects. Server overload problems can occur when a system fails to process data and queries in real-tme, which can lead to critical issues such as unbounded delay accumulation, lost monitoring accuracy or lack of spontaneity. These problems can be addressed by adopting suitable load dropping decisions. In this work, we demonstrate several load shedding techniques, focusing on decision-making based on data attributes. With the end results being quantified and visualized using real data for a large city, this proof-of-concept system provides a convincing way of validating our ideas.