jidong chen xiaofeng meng yanyan guo s.grumbach hui sun

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