Where Next? Data Mining Techniques and Challenges for Trajectory Prediction Slides credit: Layla Pournajaf
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
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
Raw Trajectories Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009
Raw Trajectories Preprocessed Trajectories Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009
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
Source: www.openstreetmap.org
Real-world data include raw trajectories of continuous GPS coordinates which are noisy and inaccurate! Source: www.openstreetmap.org
Raw Trajectories Preprocessed Trajectories Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009
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
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
Map of Beijing with 30 30 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.
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.
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
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
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)
Source: Xue, Andy Yuan, et al. "Destination prediction by sub-trajectory synthesis and privacy protection against such prediction." ICDE 2013.
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.
Source: Xue, Andy Yuan, et al. "Destination prediction by sub-trajectory synthesis and privacy protection against such prediction." ICDE 2013.
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
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)
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
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)
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
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
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
<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
<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
<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
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
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
Three steps: 1. Search for best match 2. Candidate generation 3. Make predictions Best Match Prediction
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
o Prediction errors (distance and time) o Prediction accuracy (precision and recall) o Prediction rate
H. Jeung, Q. Liu, H. T. Shen, and X. Zhou. A hybrid prediction model for moving objects. In Data Engineering, 2008. ICDE 2008. IEEE 24 th International Conference on, pages 70-79. IEEE, 2008. J. Krumm and E. Horvitz. Predestination: Inferring destinations from partial trajectories. In Ubiquitous Computing, pages 243-260. Springer, 2006. 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 637-646. ACM, 2009. G. Yavas, D. Katsaros, O. Ulusoy, and Y. Manolopoulos. A data mining approach for location prediction in mobile environments. Data & Knowledge Engineering, 54(2):121-146, 2005. 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 611-622. ACM, 2004. 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 254-265. IEEE, 2013.