Near-Lossless Compression for Large Traffic Networks
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 mo...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/81369 http://hdl.handle.net/10220/39534 |
| _version_ | 1826121098746396672 |
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| author | Muhammad Tayyab Asif Srinivasan, Kannan Mitrovic, Nikola Dauwels, Justin Jaillet, Patrick |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Muhammad Tayyab Asif Srinivasan, Kannan Mitrovic, Nikola Dauwels, Justin Jaillet, Patrick |
| author_sort | Muhammad Tayyab Asif |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T05:27:10Z |
| format | Journal Article |
| id | ntu-10356/81369 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:27:10Z |
| publishDate | 2016 |
| record_format | dspace |
| spelling | ntu-10356/813692022-09-23T01:40:25Z Near-Lossless Compression for Large Traffic Networks Muhammad Tayyab Asif Srinivasan, Kannan Mitrovic, Nikola Dauwels, Justin Jaillet, Patrick School of Electrical and Electronic Engineering Low-dimensional models Near-lossless compression 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. Accepted version 2016-01-04T05:33:53Z 2019-12-06T14:29:27Z 2016-01-04T05:33:53Z 2019-12-06T14:29:27Z 2014 Journal Article 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. 1524-9050 https://hdl.handle.net/10356/81369 http://hdl.handle.net/10220/39534 10.1109/TITS.2014.2374335 en IEEE Transactions on Intelligent Transportation Systems © 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: [http://dx.doi.org/10.1109/TITS.2014.2374335]. 10 p. application/pdf |
| spellingShingle | Low-dimensional models Near-lossless compression Muhammad Tayyab Asif Srinivasan, Kannan Mitrovic, Nikola Dauwels, Justin Jaillet, Patrick Near-Lossless Compression for Large Traffic Networks |
| title | Near-Lossless Compression for Large Traffic Networks |
| title_full | Near-Lossless Compression for Large Traffic Networks |
| title_fullStr | Near-Lossless Compression for Large Traffic Networks |
| title_full_unstemmed | Near-Lossless Compression for Large Traffic Networks |
| title_short | Near-Lossless Compression for Large Traffic Networks |
| title_sort | near lossless compression for large traffic networks |
| topic | Low-dimensional models Near-lossless compression |
| url | https://hdl.handle.net/10356/81369 http://hdl.handle.net/10220/39534 |
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