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Title: Near-Lossless Compression for Large Traffic Networks
Authors: Muhammad Tayyab Asif
Srinivasan, Kannan
Mitrovic, Nikola
Dauwels, Justin
Jaillet, Patrick
Keywords: Low-dimensional models
Near-lossless compression
Issue Date: 2014
Source: Muhammad Tayyab Asif, K., Mitrovic, N., Dauwels, J., & Jaillet, P. (2014). Near-Lossless Compression for Large Traffic Networks. IEEE Transactions on Intelligent Transportation Systems, 16(4), 1817-1826.
Series/Report no.: IEEE Transactions on Intelligent Transportation Systems
Abstract: With advancements in sensor technologies, intelligent transportation systems (ITS) can collect traffic data with high spatial and temporal resolution. However, the size of the networks combined with the huge volume of the data puts serious constraints on the system resources. Low-dimensional models can help ease these constraints by providing compressed representations for the networks. In this study, we analyze the reconstruction efficiency of several low-dimensional models for large and diverse networks. The compression performed by low-dimensional models is lossy in nature. To address this issue, we propose a near-lossless compression method for traffic data by applying the principle of lossy plus residual coding. To this end, we first develop low-dimensional model of the network. We then apply Huffman coding in the residual layer. The resultant algorithm guarantees that the maximum reconstruction error will remain below a desired tolerance limit. For analysis, we consider a large and heterogeneous test network comprising of more than 18000 road segments. The results show that the proposed method can efficiently compress data obtained from a large and diverse road network, while maintaining the upper bound on the reconstruction error.
ISSN: 1524-9050
DOI: 10.1109/TITS.2014.2374335
Schools: School of Electrical and Electronic Engineering 
Rights: © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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