Near-Lossless Compression for Large Traffic Networks
With advancements in sensor technologies, intelligent transportation systems 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 system resources. Low-dimensional models can help e...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2016
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| Online Access: | http://hdl.handle.net/1721.1/100716 https://orcid.org/0000-0002-8585-6566 |
| _version_ | 1811092093868703744 |
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| author | Asif, Muhammad Tayyab Srinivasan, Kannan Mitrovic, Nikola Dauwels, Justin Jaillet, Patrick |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Asif, Muhammad Tayyab Srinivasan, Kannan Mitrovic, Nikola Dauwels, Justin Jaillet, Patrick |
| author_sort | Asif, Muhammad Tayyab |
| collection | MIT |
| description | With advancements in sensor technologies, intelligent transportation systems 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 system resources. Low-dimensional models can help ease these constraints by providing compressed representations for the networks. In this paper, 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 a low-dimensional model of the network. We then apply Huffman coding (HC) 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 18 000 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 | 2024-09-23T15:12:53Z |
| format | Article |
| id | mit-1721.1/100716 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:12:53Z |
| publishDate | 2016 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1007162022-09-29T13:26:50Z Near-Lossless Compression for Large Traffic Networks Asif, Muhammad Tayyab Srinivasan, Kannan Mitrovic, Nikola Dauwels, Justin Jaillet, Patrick Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Operations Research Center Jaillet, Patrick With advancements in sensor technologies, intelligent transportation systems 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 system resources. Low-dimensional models can help ease these constraints by providing compressed representations for the networks. In this paper, 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 a low-dimensional model of the network. We then apply Huffman coding (HC) 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 18 000 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. Singapore. National Research Foundation (Singapore-MIT Alliance for Research and Technology Center. Future Urban Mobility Program) 2016-01-06T14:37:49Z 2016-01-06T14:37:49Z 2015-07 Article http://purl.org/eprint/type/JournalArticle 1524-9050 1558-0016 http://hdl.handle.net/1721.1/100716 Asif, Muhammad Tayyab, Kannan Srinivasan, Nikola Mitrovic, Justin Dauwels, and Patrick Jaillet. “Near-Lossless Compression for Large Traffic Networks.” IEEE Transactions on Intelligent Transportation Systems 16, no. 4 (August 2015): 1817–1826. https://orcid.org/0000-0002-8585-6566 en_US http://dx.doi.org/10.1109/tits.2014.2374335 IEEE Transactions on Intelligent Transportation Systems Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) MIT web domain |
| spellingShingle | Asif, Muhammad Tayyab 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 |
| url | http://hdl.handle.net/1721.1/100716 https://orcid.org/0000-0002-8585-6566 |
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