Exploring Spatial-Temporal Trajectory Model for Location Prediction

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica

http://angus-fuming-huang.blogspot.com/

Exploring Spatial-Temporal Trajectory Model for Location Prediction

2011.11.23

TMSG- Paper Reading


http://angus-fuming-huang.blogspot.com/2

Agenda• Authors & Publication• Paper Presentation• My Comments



Authors & Publication• Wen-Chih Peng (彭文志 )

– http://people.cs.nctu.edu.tw/~wcpeng/ – Advanced Database System Lab– http://db.csie.nctu.edu.tw/ – Best Student Paper Award

• IEEE MDM2011– http://mdmconferences.org/mdm2011/

http://people.cs.nctu.edu.tw/~wcpeng/

http://people.cs.nctu.edu.tw/~wcpeng/

http://db.csie.nctu.edu.tw/

http://db.csie.nctu.edu.tw/

http://mdmconferences.org/mdm2011/

http://mdmconferences.org/mdm2011/



Paper Outline• Introduction • Related works• Framework• Model• Prediction • Experiments• Conclusion



Introduction • Location prediction problem

– Given an object’s recent movements and a future time, the location of this object at the future time is estimated



Motivation11:30?

T1勝出 !!



Related works• Next movement

– Markov chain– Motion functions

• Granularity problem– Density-based– Grid-based

• Pattern recognition– Trajectory mining



The framework of location prediction using STT model

• Frequent region discovery– Sufficient number of data points

• Trajectory transformation– Region-based moving sequence

• STT model construction– Probabilistic suffix tree– Transition probability– Appearing probability

PST



The framework of location prediction using STT model (contd.)



Spatial-temporal trajectory model construction

• Frequent region discovery and trajectory transformation– Def. 1: Frequent Region– Def. 2: Region-based Moving Sequence

• Spatial-temporal trajectory model construction– Predictive table: spatial and temporal correlation between the region

and next movement

– Transition time interval: ik+1 = (mean, sd)

– MinSup: minimal support segment count in a region– Object moving time: Gaussian distribution



Frequent region discovery

• Eps: the neighborhood number of a given radius• MinTs: minimum number of points



Trajectory transformation

MinSup = 6 !!



Spatial-temporal trajectory model construction


http://angus-fuming-huang.blogspot.com/

STT model

14



Location prediction using STT model

• Prediction concept– To find the best next movement literally until the query time is reached

• Kernel methods– Movement similarity– Moving potential– Location prediction



Movement similarity• To search a best similar node between query sequence

and STT node

• Measuring the similarity of a labeled sequence of a tree node nk of STT and the moving sequence sq

– i is the longest common suffix of nk and sq

– The more recent movements have greater effect on future movements

• Sq =abc ; Patterns: a(0.07), b(0.27), c(0.64), bc(0.91), ab(0.34)



Moving potential• To calculate the next movement candidates of the

best similar node located

• Measuring the spatial and temporal relationship simultaneously

– Prospatial : Conditional probability

– Protemporal : Chebyshev’s inequality 2

11)(

kkXP



Moving potential (contd.)

• Arrival time te = current time tc + average transition interval mean

• Temporal error: Minimum difference of te and the representative time tk+1 of next movement candidates

• Example:

• Next movement of nk: ik+1=(5,2)

• tk+1={12:00, 15:00, 17:00}

• If the current time is 11:52• ================================• Arrival time = 11:52 + 5 = 11:57• Minimum temporal error = |11:57-12:00|=3

• Protemporal = (2^2) / (3^2) = 0.44



Location prediction



Location prediction (contd.)

1 (1x1)



Experiments• Experimental setting• Prediction accuracy comparison• Storage requirements comparison• Sensitivity analysis of parameters



Experimental setting• CarWeb

– http://carweb.cs.nctu.edu.tw/carweb/– Authors’ work published in 2008– A real car trajectory dataset– Hsinchu city, Taiwan

• RunSaturday– http://www.runsaturday.com– Collect training paths of sports hobbyists– Walk, run, bike

http://carweb.cs.nctu.edu.tw/carweb/

http://www.runsaturday.com/



Prediction accuracy comparison• E1: To verify the prediction accuracy of STT can be

improved by using grid-based clustering approach– STT-Grid vs. STT-DBSCAN– Test 150 queries– Prediction error



Prediction accuracy comparison (contd.)

• E2: Prediction performance comparison– STT vs. HPM (Hybrid Prediction Model)– An association rule-based pattern prediction approach– Under the various MinTs– Prediction error



Storage requirements comparison

• HPM dramatically grows with the MinTs• STT using data structure of suffix tree can compress the

number of sequential patterns



Sensitivity analysis of parameters



Sensitivity analysis of parameters (contd.)









Conclusion • To discover frequent movement patterns• To answer predictive queries• To reduce the pattern storage size

• A spatial-temporal trajectory model– Capture an object’s moving behavior– Forecast its future locations



My Comments• Strengths~

– Well paper structure– Well representative illustrations– Abundant experiments

• Accuracy + storage + sensitivity– Transition probability + Appearing probability

• Be a more sophisticated trajectory formation



My comments (contd.)

• Weaknesses~– Too many repeated sentences– No future work suggestions– The definition / interval of the RECENT

movement is vague– The sentence (assumption) needs to be verified (by

experiments)• “The more recent movements have greater

effect on future movements”




• Doubt~– Frequent region detection:: Order issue vs. MinSup ?




• Insight~– Different mobility modes reflect different movement patterns number

• Arbitrary vs. Limited• Different prediction design

– Reduce patterns number– Promote prediction accuracy



Thanks for your listening………..

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Exploring Spatial-Temporal Trajectory Model for Location Prediction