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

Bashir, Maryam; Ashraf, Jawad; Habib, Asad; Muzammil, Muhammad

doi:10.1002/ett.3886

Cited by 6 publications

(5 citation statements)

References 51 publications

(49 reference statements)

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“…Studies [3][4][5][6] have shown that, without compression and at 10-s collection intervals, 100 megabytes (MB) are stored for every 400 objects in a single day. Longer-term studies highlight that if you collect movement data from 10,000 users based on their geographic position every 15 s, you generate more than 50 million data per day and approximately 20 trillion data per year [7][8][9].…”

Section: Related Workmentioning

confidence: 99%

Batch Simplification Algorithm for Trajectories over Road Networks

Reyes¹,

Estrada²,

Tolozano-Benites³

et al. 2023

Preprint

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The volume of vehicular traffic in large cities has increased in recent years, the devices that collect vehicular GPS data such as cameras, GPS receivers and others generate millions of records at every instant of time generating problems in processing and storage of these data which becomes important for researchers. Intelligent Transportation Systems perform vehicle monitoring and control by collecting GPS trajectories, this large volume of information is necessary to have an optimal storage process. Its processing by means of compression techniques and simplification algorithms allow to reduce the necessary storage space. This paper presents a GPS trajectory simplification algorithm that considers noise reduction, point simplification and analysis of road network information. The results obtained on two data sets from the cities of California and Beijing are satisfactory, achieving a higher compression ratio without affecting data quality

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Section: Related Workmentioning

confidence: 99%

Batch Simplification Algorithm for Trajectories over Road Networks

Reyes¹,

Estrada²,

Tolozano-Benites³

et al. 2023

Preprint

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“…Coresets, 34 AACAT, 30 SimpleTrack, 31 SGTCR-CS 27 Probabilistic IMM, [35][36][37] APSOS, 38 SAS, 32 SAOTS, 33 SGTCR-CS 27 Graph Distance Bellman, 39,40 DOTS, 41 DOTS-CASCADE, 41 Iri-Imai, 42,43 MRPA, 44 Daescu, 45,46 OGPC and OSPC, 47 MMTC-offline, 48 MMTC-online, 48 SPPA, 49 GRTSOpt, 50 Latecki, 51 Trajic, 52 Representativeness, 53 KAA and StreamKAA, 54 OLTS and OPTTS, 55 DOTS*, 56 OSC and OSTC, 28 CLEAN 57 Angle VTracer, 58 DPTS + , 59 Latecki, 51 61 GRPPA, 62 TSHL, 63 AMS, 16 CFF, 64 BOPW and NOPW, 4 OHTA, OnlineOHTA and SATA, 65 CDR, CDRm, GRTSOpt and GRTSSec, 50 TraClus, 66 OPERB and A-OPERB, 67 BQS, 68 ABQS, FBQS and PBQS, 69 LO-OPW-TR, 70 OPW-TR, 3 SMoT, 71 Pan, 72 Patroumpas,…”

Section: Transformmentioning

confidence: 99%

“…Multiple trajectory compression Similarity, 113 TrajStore, 151 Representativeness, 53 NaTS 144 Lossless trajectory compression PRESS, 25 COMPRESS, 96 CoTracks, 90 Trajic, 52 TrajStore, 151 IFC, 152 Lovell, 12,153 LWZ 154 Network road constrained PoI and PoIE, 61 Nonmaterial, 155 VTracer, 58 TSHL, 63 OGPC and OSPC, 47 Opheimimproved, 95 , RSLC and TSLC, 96 CFF, 64 MMTC-offline and MMTC-online, 48 FFDP and FFUS, 97 GS, 108 IC-MBR, 125 SUTC, 89 INCM, 118 STTrace and Thresholds, 75 ESTC-EDP, 119 STC, 22 BTC and HTC, 25 STMaker, 103,104 SNDSC, 122 CLEAN 57…”

Section: Special Approach Techniquesmentioning

confidence: 99%

“…PoI and PoIE, 61 TS, 127,128 2 stage-pls, 126 IMM, [35][36][37] OGPC and OSPC, 47 FFDP and FFUS, 97 S-DMin and SE-DMin, 129 ATS, 117 SMoT, 71 CB-SMoT, 21 Patroumpas, 73,74 STC, 22 RGRASP-SemTS, 24 BTC and HTC, 25 STMaker, 103,104 SELF, 121 SetraStream, 82 HESAVE and SNDSC, 122 SPD 84 then, using these, other algorithm is applied to select the measurements.…”

Section: Semantic Segmentsmentioning

confidence: 99%

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Review and classification of trajectory summarisation algorithms: From compression to segmentation

Amigo

Pedroche

García

et al. 2021

International Journal of Distributed Sensor Networks

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With the continuous development and cost reduction of positioning and tracking technologies, a large amount of trajectories are being exploited in multiple domains for knowledge extraction. A trajectory is formed by a large number of measurements, where many of them are unnecessary to describe the actual trajectory of the vehicle, or even harmful due to sensor noise. This not only consumes large amounts of memory, but also makes the extracting knowledge process more difficult. Trajectory summarisation techniques can solve this problem, generating a smaller and more manageable representation and even semantic segments. In this comprehensive review, we explain and classify techniques for the summarisation of trajectories according to their search strategy and point evaluation criteria, describing connections with the line simplification problem. We also explain several special concepts in trajectory summarisation problem. Finally, we outline the recent trends and best practices to continue the research in next summarisation algorithms.

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“…When the time interval between two adjacent track points is long, it is usually because no signal in the path has been recorded for a long time although the object is still in motion or the object stayed for a long time ( Bashir et al, 2022 ; Lin et al, 2021 ). This is a relatively complex situation and can occur when the signal is lost because of the occlusion of buildings or mountains or when the moving objects move underground ( Kubicka et al, 2018 ; Koller et al, 2015 ), even if the object is attracted to something and stops moving forward, these situations are worthy of attention for data mining, so these points should be preserved.…”

Section: Introductionmentioning

confidence: 99%

A trajectory data compression algorithm based on spatio-temporal characteristics

Zhong

Kong

Zhang

et al. 2022

PeerJ Computer Science

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Background With the growth of trajectory data, the large amount of data causes a lot of problems with storage, analysis, mining, etc. Most of the traditional trajectory data compression methods are focused on preserving spatial characteristic information and pay little attention to other temporal information on trajectory data, such as speed change points or stop points. Methods A data compression algorithm based on the spatio-temporal characteristics (CASC) of the trajectory data is proposed to solve this problem. This algorithm compresses trajectory data by taking the azimuth difference, velocity difference and time interval as parameters in order to preserve spatial-temporal characteristics. Microsoft’s Geolife1.3 data set was used for a compression test to verify the validity of the algorithm. The compression results were compared with the traditional Douglas-Peucker (DP), Top-Down Time Ratio (TD-TR) and Opening Window (OPW) algorithms. Compression rate, the direction information of trajectory points, vertical synchronization distance, and algorithm type (online/offline) were used to evaluate the above algorithms. Results The experimental results show that with the same compression rate, the ability of the CASC to retain the forward direction trajectory is optimal, followed by TD-TR, DP, and then OPW. The velocity characteristics of the trajectories are also stably retained when the speed threshold value is not more than 100%. Unlike the DP and TD-TR algorithms, CASC is an online algorithm. Compared with OPW, which is also an online algorithm, CASC has better compression quality. The error distributions of the four algorithms have been compared, and CASC is the most stable algorithm. Taken together, CASC outperforms DP, TD-TR and OPW in trajectory compression.

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

Cited by 6 publications

References 51 publications

Batch Simplification Algorithm for Trajectories over Road Networks

Batch Simplification Algorithm for Trajectories over Road Networks

Review and classification of trajectory summarisation algorithms: From compression to segmentation

A trajectory data compression algorithm based on spatio-temporal characteristics

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