Received: 29 November 2019 Revised: 2 January 2020 Accepted: 10 January 2020 DOI: 10.1002/ett.3886 SPECIAL ISSUE ARTICLE An intelligent linear time trajectory data compression framework for smart planning of sustainable metropolitan cities Maryam Bashir 1 Jawad Ashraf 1 Asad Habib 1 Muhammad Muzammil 2 1 Institute of Computing, Kohat University of Science and Technology, Kohat, Pakistan 2 Department of Computer Sciences, Bahria University, Islamabad, Pakistan Correspondence Asad Habib, Kohat University of Science and Technology, Kohat, Pakistan. Email:asadhabib@kust.edu.pk Abstract The urban road networks and vehicles generate exponential amount of spatio-temporal big-data, which invites researchers from diverse fields of inter- est. 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 pat- terns. This sporadically stored big-data contains invaluable information, which is useful for a number of real-time applications. Compression is a highly impor- tant, but knotty task. Optimized compression enables us achieve the desired results in efficient and effective manner by using minimum energy and compu- tational 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 com- presses 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 Compr 10 reduced these trajectories to 17 428, 11 084, and 6565, respectively. Results of Compr 15 and Compr 20 show promising results. We define the quality of the compres- sion in context of the considered problem. The results show that the proposed technique achieved satisfactory quality of the compression. 1 INTRODUCTION The urban roads networks and many types of vehicles running over them generate exponential amount of spatio-temporal big-data, which attracts the keen interest of researchers from diverse fields of research such as power and energy, geographical information systems, data mining, artificial intelligence, and so on. This big-data contains invaluable This is a companion to [10.1002/ett.3886]. Trans Emerging Tel Tech. 2020;e3886. wileyonlinelibrary.com/journal/ett © 2020 John Wiley & Sons, Ltd. 1 of 14 https://doi.org/10.1002/ett.3886