Research Article
An Uncertain Trajectory Modelling Method Based on Kernel Density Estimation
@INPROCEEDINGS{10.4108/eai.27-8-2020.2296731, author={Yuan Cheng and Ronghua Chi and Yahong Wang}, title={An Uncertain Trajectory Modelling Method Based on Kernel Density Estimation}, proceedings={Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020, 27-28 August 2020, Cyberspace}, publisher={EAI}, proceedings_a={MOBIMEDIA}, year={2020}, month={11}, keywords={uncertainties kernel density estimation modelling method distribution characteristics}, doi={10.4108/eai.27-8-2020.2296731} }- Yuan Cheng
Ronghua Chi
Yahong Wang
Year: 2020
An Uncertain Trajectory Modelling Method Based on Kernel Density Estimation
MOBIMEDIA
EAI
DOI: 10.4108/eai.27-8-2020.2296731
Abstract
The accurate analysis of trajectories is of great significance for route selection, traffic status analysis, and urban traffic planning and so on. Existing researches lack effective methods for dealing with possible uncertainties in trajectories caused by objective enviroment and subjective intention etc. This work studies the method of constructing an uncertain model for the trajectories with the same starting point and end point based on kernel density estimation, to discover the distribution characteristics of the trajectories between two points in historical data, and to lay the foundation for trajectory prediction. Finally, the validity of the proposed method is verified on the real trajectory dataset.
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