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/

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/

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

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

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

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

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………..

Exploring Spatial-Temporal Trajectory Model for Location Prediction

Documents

Automating Trajectory Prediction Performance Analyses for ... · Automating Trajectory Prediction Performance Analyses for the FAA Traffic Management Advisor ... (4D) trajectories

Introvert: Human Trajectory Prediction via Conditional 3D

Multi-timescale Trajectory Prediction for Abnormal …Multi-timescale Trajectory Prediction for Abnormal Human Activity Detection Royston Rodrigues Neha Bhargava Rajbabu Velmurugan

Semantic Trajectory Mining for Location Prediction

EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning … · EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning Jiachen Li

Multi-Agent Trajectory Prediction and Generation with

Trajectory Prediction for a Typical Fin Stabilized

Probabilistic Vehicle Trajectory Prediction over Occupancy

Social LSTM: Human Trajectory Prediction in Crowded Spaces

TRAJECTORY PREDICTION UNCERTAINTY MODELING FOR …

Trajectory Clustering for Motion Prediction

TNT: Target-driveN Trajectory Prediction

Cruise Missile Target Trajectory Movement Prediction based

The Impact of Trajectory Prediction Uncertainty on

Attention Based Vehicle Trajectory Prediction

Trajectory Prediction for Autonomous Driving based on

PREDICTION OE STORE TRAJECTORY DROPPED M.B.KANDIEL …

CameraAlignmentusingTrajectoryIntersectionsinUnsynchroniz ......the temporal and spatial thresholds that denote when two trajectory points are close to each other. 3.2. Temporal Trajectory

STINet: Spatio-Temporal-Interactive Network for Pedestrian ......STINet: Spatio-Temporal-Interactive Network for Pedestrian Detection and Trajectory Prediction Zhishuai Zhang1,2∗

Latent Terrain Representations for Trajectory Prediction