An intelligent linear time trajectory data compression framework for smart planning of sustainable metropolitan cities

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dc.contributor.authorBashir, Maryam
dc.contributor.authorAshraf, Jawad
dc.contributor.authorHabib, Asad
dc.contributor.authorMuzammil, Muhammad
dc.date.accessioned2024-10-29T14:43:06Z
dc.date.available2024-10-29T14:43:06Z
dc.date.issued2020-02-10
dc.description.abstractThe urban road networks and vehicles generate exponential amount of spatio‐temporal big‐data, which invites researchers from diverse fields of interest. Global positioning system devices may transceive data every second thus producing huge amount of trajectory data. Subsequently, it requires optimized computing for various operations such as visualization and mining hidden patterns. This sporadically stored big‐data contains invaluable information, which is useful for a number of real‐time applications. Compression is a highly important, but knotty task. Optimized compression enables us achieve the desired results in efficient and effective manner by using minimum energy and computational resources without compromising on important information. We present two versions of a compression technique based on the points of intersections (PoI) of urban roads networks. Based on intelligent mining paradigm, we created a compressed lookup lexicon to store the PoIs of dynamically selected region of interests (ROI). An important feature of our lexicon is the key pattern, which is intelligently computed based on the relative geographic position of a spatial geodetic vertex with respect to Euclidean space origin in a given ROI. This compresses trajectories in linear time, making it feasible for mission critical real world applications. Our experimental dataset contained 959 547, 517 436, and 231 740 trajectories for Bikes, Cars, and Taxis, respectively. The Compr10 reduced these trajectories to 17 428, 11 084, and 6565, respectively. Results of Compr15 and Compr20 show promising results. We define the quality of the compression in context of the considered problem. The results show that the proposed technique achieved satisfactory quality of the compression.
dc.exception.ref2021codes252c
dc.funderNo external funder
dc.identifier.citationBashir, M., Ashraf, J., Habib, A., Muzammil, M. (2022) An intelligent linear time trajectory data compression framework for smart planning of sustainable metropolitan cities. Transactions on Emerging Telecommunications Technologies, 33 (2), e3886
dc.identifier.doihttps://doi.org/10.1002/ett.3886
dc.identifier.issn2161-3915
dc.identifier.issn2161-3915
dc.identifier.urihttps://hdl.handle.net/2086/24411
dc.language.isoen
dc.peerreviewedYes
dc.publisherWiley
dc.relation.ispartofTransactions on Emerging Telecommunications Technologies
dc.titleAn intelligent linear time trajectory data compression framework for smart planning of sustainable metropolitan cities
dc.typeArticle
oaire.citation.issue2
oaire.citation.volume33

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