Global Positionning System (GPS) trajectory is an ordered list of GPS points, which are approximate since they depend on the quality of the GPS sensor and the covering satellites. Finding common frequent sub-trajectories in a given trajectories database enables to detect what are the most used paths encapsulating the objects behaviours. Most trajectories mining algorithms proposed in the literature require a preprocessing discretization step where the plan is discretized into tile blocks, enabling to use classical sequential mining algorithms. However, this step is time consuming and improper for real time applications. In this paper, we propose an algorithm, named TrajGrowth, which directly works on the raw data, without any preprocessing step and without requiring a laborious parameter setting for its execution. Clearly, instead the costly discretization step of standard approaches, we used a precision parameter for which low values push down the mining process to find more precise patterns. The experimental results show that our proposed approach is more precise than the discretization based approaches with a better processing time and avoiding redundant patterns.
ZhengY.ZhangL.XieX. and MaW.-Y., Mining interesting locations and travel sequences from gps trajectories, in: Proceedings of the 18th International Conference on World Wide Web, ACM, 2009, pp. 791–800.
2.
ElragalA. and El-GendyN., Trajectory data mining: Integrating semantics, Journal of Enterprise Information Management26(5) (2013), 516–535.
3.
FengZ. and ZhuY., A survey on trajectory data mining: Techniques and applications, IEEE Access4 (2016), 2056–2067.
4.
ZhengY. and ZhouX., Computing with spatial trajectories, 2011, 06–08.
5.
YuanJ.ZhengY.XieX. and SunG., T-drive: Enhancing driving directions with taxi drivers’ intelligence, IEEE Transactions on Knowledge and Data Engineering25(1) (2013), 220–232.
6.
ShawA.A. and GopalanN., Finding frequent trajectories by clustering and sequential pattern mining, Journal of Traffic and Transportation Engineering (English Edition)1(6) (2014), 393–403.
7.
MazimpakaJ.D. and TimpfS., Trajectory data mining: A review of methods and applications, Journal of Spatial Information Science2016(13) (2016), 61–99.
8.
YaoD.ZhangC.ZhuZ.HuangJ. and BiJ., Trajectory clustering via deep representation learning, in: Neural Networks (IJCNN), 2017 International Joint Conference on, IEEE, 2017, pp. 3880–3887.
9.
LiX.PanG.WuZ.QiG.LiS.ZhangD.ZhangW. and WangZ., Prediction of urban human mobility using large-scale taxi traces and its applications, Frontiers of Computer Science6(1) (2012), 111–121.
10.
BerminghamL. and LeeI., A general methodology for n-dimensional trajectory clustering, Expert Systems with Applications42(21) (2015), 7573–7581.
11.
TrasartiR.PinelliF.NanniM. and GiannottiF., Individual mobility profiles: Methods and application on vehicle sharing, in: SEBD, 2012, pp. 35–42.
12.
Monteiro de LiraV.RensoC.PeregoR.RinzivilloS. and Cesario TimesV., The comewithme system for searching and ranking activity-based carpooling rides, in: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, 2016, pp. 1145–1148.
13.
BrakatsoulasS.PfoserD.SalasR. and WenkC., On map-matching vehicle tracking data, in: Proceedings of the 31st International Conference on Very Large Data Bases, VLDB Endowment, 2005, pp. 853–864.
14.
LouY.ZhangC.ZhengY.XieX.WangW. and HuangY., Map-matching for low-sampling-rate gps trajectories, in: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, 2009, pp. 352–361.
15.
NewsonP. and KrummJ., Hidden markov map matching through noise and sparseness, in: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, 2009, pp. 336–343.
16.
WangL.HuK.KuT. and YanX., Mining frequent trajectory pattern based on vague space partition, Knowledge-based Systems50 (2013), 100–111.
17.
HanJ.PeiJ.Mortazavi-AslB.PintoH.ChenQ.DayalU. and HsuM., Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth, in: Proceedings of the 17th International Conference on Data Engineering, 2001, pp. 215–224.
18.
de WeerdN.van LangeveldeF.van OeverenH.NoletB.A.KölzschA.PrinsH.H. and de BoerW.F., Deriving animal behaviour from high-frequency gps: Tracking cows in open and forested habitat, Plos One10(6) (2015), 29–30.
19.
LeeJ.-G.HanJ. and WhangK.-Y., Trajectory clustering: A partition-and-group framework, in: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, ACM, 2007, pp. 593–604.
20.
HeT.BaoJ.RuanS.LiR.LiY.HeH. and ZhengY., Interactive bike lane planning using sharing bikes’ trajectories, in: IEEE Transactions on Knowledge and Data Engineering, 2019, pp. 04–06.
21.
GiannottiF.NanniM.PinelliF. and PedreschiD., Trajectory pattern mining, in: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2007, pp. 330–339.
22.
KangJ.-Y. and YongH.-S., Mining spatio-temporal patterns in trajectory data, Journal of Information Processing Systems6(4) (2010), 521–536.
23.
SanniM. and AkbarS., Trajectory pattern mining with multistage spatial partitioning, International Journal on Electrical Engineering & Informatics9(2) (2017), 382–393.
24.
MasciariE.ShiG. and ZanioloC., Sequential pattern mining from trajectory data, in: Proceedings of the 17th International Database Engineering & Applications Symposium, ACM, 2013, pp. 162–167.
25.
ChungJ.D.PaekO.H.LeeJ.W. and RyuK.H., Temporal pattern mining of moving objects for location-based service, in: Proceedings of the 13th International Conference on Database and Expert Systems Applications, Springer-Verlag, 2002, pp. 331–340.
GidófalviG. and PedersenT.B., Mining long, sharable patterns in trajectories of moving objects, Geoinformatica13(1) (2009), 27–55.
28.
BayirM.A.DemirbasM. and EagleN., Mobility profiler: A framework for discovering mobility profiles of cell phone users, Pervasive and Mobile Computing6(4) (2010), 435–454.
29.
ZakiM.J., SPADE: An efficient algorithm for mining frequent sequences, Machine Learning42(1/2) (2001), 31–60. [Online]. Available: doi: 10.1023/A:1007652502315.
30.
HanJ.PeiJ.YinY. and MaoR., Mining frequent patterns without candidate generation: A frequent-pattern tree approach, Data Mining and Knowledge Discovery8(1) (2004), 53–87.
31.
AgrawalR.SrikantR. et al., Fast algorithms for mining association rules, in: Proc. 20th Int. Conf. Very Large Data Bases, VLDB, Vol. 1215, 1994, pp. 487–499.
32.
FuZ.TianZ.XuY. and ZhouK., Mining frequent route patterns based on personal trajectory abstraction, IEEE Access5 (2017), 11352–11363.
33.
LvM.LiY.YuanZ. and WangQ., Route pattern mining from personal trajectory data, J. Inf. Sci. Eng.31(1) (2015), 147–164.
34.
ZhangX. and YuanZ., The gps trajectory data research based on the intelligent traffic big data analysis platform, Journal of Computational Methods in Sciences and Engineering17(3) (2017), 423–430.
35.
MaH. and ZhouX., A gps location data clustering approach based on a niche genetic algorithm and hybrid k-means, Intelligent Data Analysis23(S1) (2019), 175–198.
36.
AltH. and GodauM., Computing the fréchet distance between two polygonal curves, International Journal of Computational Geometry & Applications5(01–02) (1995), 75–91.
37.
ZhuH.LuoJ.YinH.ZhouX.HuangJ.Z. and ZhanF.B., Mining trajectory corridors using fréchet distance and meshing grids, in: Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, 2010, pp. 228–237.
38.
BuchinM.DodgeS. and SpeckmannB., Similarity of trajectories taking into account geographic context, Journal of Spatial Information Science2014(9) (2014), 101–124.
39.
YuanG.SunP.ZhaoJ.LiD. and WangC., A review of moving object trajectory clustering algorithms, Artificial Intelligence Review47(1) (2017), 123–144.
40.
HanJ. and PeiJ., Mining frequent patterns by pattern-growth: Methodology and implications, ACM SIGKDD Explorations Newsletter2(2) (2000), 14–20.
41.
LiuL. and ÖzsuM.T., in: Encyclopedia of Database Systems, Springer New York, NY, USA, Vol. 6, 2009.
42.
PeiJ., Seqpatternminer: Mining sequential patterns by prefix-projected growth, in: Proc. Int. Conf. on Data Engi-neering, 2001, 2001, pp. 2–6.
43.
ZhangC.HanJ.ShouL.LuJ. and La PortaT., Splitter: Mining fine-grained sequential patterns in semantic trajectories, Proceedings of the VLDB Endowment7(9) (2014), 769–780.
44.
KhoshahvalS.FarnaghiM. and TaleaiM., Spatio-temporal pattern mining on trajectory data using arm, International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences42 (2017), 395–399.
45.
KemmarA.LebbahY.LoudniS.BoizumaultP. and CharnoisT., Prefix-projection global constraint and top-k approach for sequential pattern mining, Constraints22(2) (2017), 265–306. [Online]. Available: doi: 10.1007/s10601-016-9252-z.
46.
FengT.ZhangY.-R.JinY. and ZhangJ., A compromise-negotiation framework based on game theory for eliminating requirements inconsistency, Tehnički Vjesnik22(5) (2015), 1085–1092.
