Modelling and Predicting Future Trajectories of Moving Objects in a Constrained Network appeared in

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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
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Outline

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

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Outline

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

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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

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Outline

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

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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

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Outline

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

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Graphs of Cellular
Automata Model (GCA)

• Modeling of the road network
• cellular automata
• nodes
• edges
• GCA state a mapping from cells to MOs, velocity

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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

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Graphs of
Cellular Automata Model (GCA)

• Moving rules
• Po

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Outline

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

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Trajectory Prediction

• The Linear Prediction (LP)
• the trajectory function for an object between
  time t0 and t1
• basic LP idea
• the inadequacy of LP

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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)

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Trajectory Prediction

• Get the slowest and the fastest movement function
  by using different Pd
• Find the bounds of future positions by
  translating the 2 regression lines

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Trajectory Prediction

• Obtain specific future position

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Outline

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

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Experimental Evaluation

• Datasets
• generated by CA simulator
• Brinkhoffs
  Network-based Generator
• Prediction Accuracy with Different Threshold

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Experimental Evaluation

• Prediction Accuracy with Different Pd

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Outline

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

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Conclusion

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

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Outline

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

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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.

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Outline

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

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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. (ti, di)
  and (ti, li)
• typoes

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• thank you