Where Next? Data Mining Techniques and Challenges for Trajectory Prediction. Slides credit: Layla Pournajaf

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1 Where Next? Data Mining Techniques and Challenges for Trajectory Prediction Slides credit: Layla Pournajaf

2 o Navigational services. o Traffic management. o Location-based advertising. Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009

3 o Destination prediction o Path prediction with known destination o Path prediction with unknown destination o Similar to predicting next N locations Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009

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5 Raw Trajectories Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009

6 Raw Trajectories Preprocessed Trajectories Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009

7 Prediction Model Raw Trajectories Preprocessed Trajectories Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009

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9 Real-world data include raw trajectories of continuous GPS coordinates which are noisy and inaccurate! Source:

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11 Raw Trajectories Preprocessed Trajectories Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009

12 o Discretizing Time o 30 seconds, one hour otemporal Representation o Location-series o Fixed-interval time-location series o Variable-interval time-location series Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009

13 Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009 o Discretizing Location o Grid-based (uniform vs hierarchical) o Mining Frequent Regions Clustering Semantic-based

14 Map of Beijing with grid overlay: Each cell 1.78km2 Source: Xue, Andy Yuan, et al. "Destination prediction by sub-trajectory synthesis and privacy protection against such prediction." ICDE 2013.

15 o o Clustering o DBScan o Hierarchical Clustering Semantic-based o Using points of interests Source: Lei, Po-Ruey, et al. "Exploring spatial-temporal trajectory model for location prediction." MDM 2011.

16 Prediction Models Raw Trajectories Preprocessed Trajectories Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009

17 Personalized / Individual-based: o Utilize only the history of one object to predict its future locations General: o Utilize the history of all objects to predict future locations

18 Model-based (formulate the movement of moving objects using mathematical models) o Markov Models o Hidden Markov Models (Zhou et. al., ACM SIGKDD 2013) o Recursive Motion Function (Y. Tao et. al., ACM SIGMOD 2004) odeep learning models Pattern-based (exploit pattern mining algorithms for prediction) o Sequential Pattern Mining (G. Yavas et. al., DKE 2005) o Trajectory Pattern Mining Hybrid o Recursive Motion Function + Sequential Pattern Mining (H. Jeung et. al., ICDE 2008)

19 Source: Xue, Andy Yuan, et al. "Destination prediction by sub-trajectory synthesis and privacy protection against such prediction." ICDE 2013.

20 2 p 45 = 3 1 p 56 = 3 Source: Xue, Andy Yuan, et al. "Destination prediction by sub-trajectory synthesis and privacy protection against such prediction." ICDE 2013.

21 Source: Xue, Andy Yuan, et al. "Destination prediction by sub-trajectory synthesis and privacy protection against such prediction." ICDE 2013.

22 Partial Trajectory: <r 1, t 1 >, <r 2, t 2 >,., <r c, t c > <?, t c+1 > Prediction: Having a partial trajectory (discretized) including the current region r c, find the most probable region at time point t c+1 arg max P(R c+1 = r c+1 r 1, r c ) r c+1

23 Embedding Higher-Order Chains Each new state depends on fixed-length window of preceding state values We can represent this as a first-order model via state augmentation: (N 2 augmented states)

24 Semi-Lazy Hidden Markov Approach (SIGKDD 13) Find similar trajectories from historical trajectories (reference objects) Build a hidden Markov Model on the fly (vs. eager or lazy approach) Self-correcting continuous prediction (real time) Refine prediction model Adjust weights for reference objects

25 Model-based (formulate the movement of moving objects using mathematical models) o Markov Models o Hidden Markov Models (Zhou et. al., ACM SIGKDD 2013) o Recursive Motion Function (Y. Tao et. al., ACM SIGMOD 2004) odeep learning models Pattern-based (exploit pattern mining algorithms for prediction) o Sequential Pattern Mining (G. Yavas et. al., DKE 2005) o Trajectory Pattern Mining (Monreale et al ACM SIGKDD 2009) Hybrid o Recursive Motion Function + Sequential Pattern Mining (H. Jeung et. al., ICDE 2008)

26 1. Preprocess raw trajectories and extract frequent sequential patterns (T-Pattern) Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009

27 1. Preprocess raw trajectories and extract frequent sequential patterns (T-Pattern) 2. Build a Prefix Tree (T-Pattern Tree) Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009

28 1. Preprocess raw trajectories and extract frequent sequential patterns (T-Pattern) 2. Build a Prefix Tree (T-Pattern Tree) 3. Predict Next Location Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009

29 <x 1, y 1, t 1 >, <x 2, y 2, t 2 >,., <x n, y n, t n > Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009

30 <x 1, y 1, t 1 >, <x 2, y 2, t 2 >,., <x n, y n, t n > Two points match if one falls within a spatial neighborhood N() of the other Two transition times match if their temporal difference is τ Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009

31 <x 1, y 1, t 1 >, <x 2, y 2, t 2 >,., <x n, y n, t n > Two points match if one falls within a spatial neighborhood N() of the other Two transition times match if their temporal difference is τ Calculate support for each T-pattern Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009

32 Generating all association rules from each T-pattern and using them to build a classifier is too expensive. T-Pattern α 1 α 2 α 3 R 1 R 2 R 3 R 4 Rules R 1 R 2 R 3 R 4 R 1 R 2 R 3 R 4 R 1 R 2 R 3 R 4 Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009

33 To avoid the rules generation, the T-Pattern set is organized as a prefix tree. For Each node v Id identifies the node v Region is a spatial component of the T-Pattern Support is the support of the T-pattern For Each edge j [a,b] correspond to the time interval α n of the T-Pattern

34 Three steps: 1. Search for best match 2. Candidate generation 3. Make predictions Best Match Prediction

35 The Best Match is the path having: the maximum path score using the time and location matching and support at least one admissible prediction. Three steps: 1. Search for best match 2. Candidate generation 3. Make predictions

36 o Prediction errors (distance and time) o Prediction accuracy (precision and recall) o Prediction rate

37 H. Jeung, Q. Liu, H. T. Shen, and X. Zhou. A hybrid prediction model for moving objects. In Data Engineering, ICDE IEEE 24 th International Conference on, pages IEEE, J. Krumm and E. Horvitz. Predestination: Inferring destinations from partial trajectories. In Ubiquitous Computing, pages Springer, A. Monreale, F. Pinelli, R. Trasarti, and F. Giannotti. Wherenext: a location predictor on trajectory pattern mining. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages ACM, G. Yavas, D. Katsaros, O. Ulusoy, and Y. Manolopoulos. A data mining approach for location prediction in mobile environments. Data & Knowledge Engineering, 54(2): , Y. Tao, C. Faloutsos, D. Papadias, and B. Liu. Prediction and indexing of moving objects with unknown motion patterns. In Proceedings of the 2004 ACM SIGMOD international conference on Management of data, pages ACM, A. Y. Xue, R. Zhang, Y. Zheng, X. Xie, J. Huang, and Z. Xu. Destination prediction by sub-trajectory synthesis and privacy protection against such prediction. In Data Engineering (ICDE), 2013 IEEE 29th International Conference on, pages IEEE, 2013.