This is a repository for the codes and other supplements for the paper entitled "Classifying Spatial Trajectories"
Hasan Pourmahmood-Aghababa and Jeff M. Phillips
This repository contains the codes used for experiments in the paper entitled "Classifying Spatial Trajectories" by Hasan Pourmahmood-Aghababa and Jeff M. Phillips from University of Utah. Figures in the paper are also given in the Figure named file.
In this paper 5 real-world widely used public datasets are utilized for experiments which we list below with a link to them. The simulated car-bus dataset is uploaded to the repository under the name Simulated Car-Bus data.
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Car-Bus dataset from UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/GPS+Trajectories
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Two persons trajectory data set from University of Illinois at Chicago: https://www.cs.uic.edu/~boxu/mp2p/gps_data.html
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Character Trajectories Data Set from UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/Character+Trajectories
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T-drive Trajectory data set released by Microsoft in 2011: https://www.microsoft.com/en-us/research/publication/driving-with-knowledge-from-the-physical-world/
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Geolife Trajectory data set released by Microsoft: https://msropendata.com/datasets/d19b353b-7483-4db7-a828-b130f6d1f035
For the purpose of reproducibility of experiments, we have included all the needed codes in this repository in order to make it easy to use.
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Some of the codes were run on Google Colab and a minority of them were run in Anaconda. These are included in ipynb named folders for each dataset experiment. The .py file of them are also given in py named folders.
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In .ipynb files the numbers from experiments and pictures of curves are those that are reported in the paper.
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The main codes are included in "Classes_Used_in_Codes" file, which are imported in almost all other codes.
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For KNN classification with Frechet, discrete Frechet, Hausdorff, LCSS, SSPD, EDR and ERP distances, we have used the efficiently written codes in GitHub page GitHub page https://github.com/bguillouet/traj-dist. The soft-dtw distance is imported from GitHub page https://github.com/mblondel/soft-dtw and fastdtw from PyPI. Dynamic Time Warping (DTW) distance is imported from tslearn, and d_Q^pi from trjtrypy package by the authors in PyPI. LSH is implemented by the authors again using trjtrypy.
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Jeff M. Phllips and H. Pourmahmood-Aghababa. Orientation-Preserving Vectorized Distance Between Curves, MSML 2021.
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Jeff M. Phillips and Pingfan Tang. Simple distances for trajectories via landmarks. In ACM GIS SIGSPATIAL, 2019.