1. Modelling and Predicting Future Trajectories of Moving Objects in a Constrained Network appeared in “Proceedings of the 7th International Conference on Mobile Data Management (MDM'06),japan” Jidong Chen Xiaofeng Meng Yanyan Guo S.Grumbach Hui Sun Information School, Renmin University of China, Beijing, China Presented by Yanfen Xu

2. 2 Outline Introduction Related Work Graphs of Cellular Automata Model (GCA) Trajectory Prediction Experimental Evaluation Conclusion Relation to our Project Strong and Weak Points

3. 3 Outline Introduction Related Work Graphs of Cellular Automata Model (GCA) Trajectory Prediction Experimental Evaluation Conclusion Relation to our Project Strong and Weak Points

4. 4 Introduction Focus:  location modelling  future trajectory prediction Contributions:  present the graphs of cellular automata (GCA) model  propose a simulation based prediction (SP) method  experiments evaluation

5. 5 Outline Introduction Related Work Graphs of Cellular Automata Model (GCA) Trajectory Prediction Experimental Evaluation Conclusion Relation to our Project Strong and Weak Points

6. 6 Related Work The modeling of MOs:  MOST model, STGS model, abstract data type  connecting road network with MOs  first in 2001, wolfson et. Al  L.Speicys: a computational data model  MODTN model Prediction methods for future trajectories  Linear movement model  Non_linear movement models, using  quadratic predictive function,  recursive motion functions  Chebyshev polynomials

7. 7 Outline Introduction Related Work Graphs of Cellular Automata Model (GCA) Trajectory Prediction Experimental Evaluation Conclusion Relation to our Project Strong and Weak Points

8. 8 Graphs of Cellular Automata Model (GCA) Modeling of the road network:  cellular automata  nodes  edges  GCA state: a mapping from cells to MOs, velocity

9. 9 Graphs of Cellular Automata Model (GCA) Modeling of the MOs  position can be expressed by (startnode, endnode, measure).  the in-edge trajectory of a MO in a CA of length L:  the global trajectory of a MO in different CAs:

10. 10 Graphs of Cellular Automata Model (GCA) Moving rules: P o

11. 11 Outline Introduction Related Work Graphs of Cellular Automata Model (GCA) Trajectory Prediction Experimental Evaluation Conclusion Relation to our Project Strong and Weak Points

12. 12 Trajectory Prediction The Linear Prediction (LP)  the trajectory function for an object between time t 0 and t 1  basic LP idea  the inadequacy of LP

13. 13 Trajectory Prediction The Simulation-based Prediction (SP)  Get the predicted positions by simulating a object  Get the future trajectory function of a MO from the points using regression (OLSE)

14. 14 Trajectory Prediction  Get the slowest and the fastest movement function by using different P d  Find the bounds of future positions by translating the 2 regression lines

15. 15 Trajectory Prediction  Obtain specific future position

16. 16 Outline Introduction Related Work Graphs of Cellular Automata Model (GCA) Trajectory Prediction Experimental Evaluation Conclusion Relation to our Project Strong and Weak Points

17. 17 Experimental Evaluation Datasets: generated by: CA simulator Brinkhoff’s Network-based Generator Prediction Accuracy with Different Threshold

18. 18 Experimental Evaluation Prediction Accuracy with Different P d

19. 19 Outline Introduction Related Work Graphs of Cellular Automata Model (GCA) Trajectory Prediction Experimental Evaluation Conclusion Relation to our Project Strong and Weak Points

20. 20 Conclusion introduce a new model - GCA propose a prediction method, based on the GCA experiments show higher performacne than linear prediction

21. 21 Outline Introduction Related Work Graphs of Cellular Automata Model (GCA) Trajectory Prediction Experimental Evaluation Conclusion Relation to our Project Strong and Weak Points

22. 22 Relation to our Project Common:  Modeling road network constrained MOs  Tracking the movement of MOs Difference:  efficiently perform query on MOs in oracle in my project  an option to use non-linear predition strategy  an idea to consider the uncertainty of MO.

23. 23 Outline Introduction Related Work Graphs of Cellular Automata Model (GCA) Trajectory Prediction Experimental Evaluation Conclusion Relation to our Project Strong and Weak Points

24. 24 Strong and Weak Points Strong Points  integrate traffic simulation techniques with dbs model  propose a GCA model  take correlation of MOs and stochastic hehavior into account Weak Points  a non-trival prediction strategy  inconsistent position representation. (t i, d i ) and (t i, l i )  typoes :

25. 25 thank you