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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 - PowerPoint PPT Presentation
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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 Brinkhoff’s 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
