Data compression techniques for urban traffic data
Author(s)Asif, Muhammad Tayyab; Kannan, Srinivasan; Dauwels, Justin H. G.; Jaillet, Patrick
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With the development of inexpensive sensors such as GPS probes, Data Driven Intelligent Transport Systems (D[superscript 2]ITS) can acquire traffic data with high spatial and temporal resolution. The large amount of collected information can help improve the performance of ITS applications like traffic management and prediction. The huge volume of data, however, puts serious strain on the resources of these systems. Traffic networks exhibit strong spatial and temporal relationships. We propose to exploit these relationships to find low-dimensional representations of large urban networks for data compression. In this paper, we study different techniques for compressing traffic data, obtained from large urban road networks. We use Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA) for 2-way network representation and Tensor Decomposition for 3-way network representation. We apply these techniques to find low-dimensional structures of large networks, and use these low-dimensional structures for data compression.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Proceedings of the 2013 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS)
Institute of Electrical and Electronics Engineers (IEEE)
Asif, Muhammad Tayyab, Srinivasan Kannan, Justin Dauwels, and Patrick Jaillet. “Data Compression Techniques for Urban Traffic Data.” 2013 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS) (n.d.).
Author's final manuscript