Data underlying the publication: Timely Adaptive Strategies for Fugitive Interception

doi:10.4121/fa299948-661f-4003-a4c1-a4f3a6bb2809.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/fa299948-661f-4003-a4c1-a4f3a6bb2809
Datacite citation style:
van Droffelaar, Irene (2024): Data underlying the publication: Timely Adaptive Strategies for Fugitive Interception. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/fa299948-661f-4003-a4c1-a4f3a6bb2809.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset

This repository is part of the Ph.D. thesis of Irene S. van Droffelaar, Delft University of Technology.


This repository accompanies the paper "Timely Adaptive Strategies for Fugitive Interception".


Folders DirectPolicySearch, OneShot, PeriodicReOpt, and PolicyTreeOpt contain the models and results for each of the solution approaches used in the paper. Each folder contains the folders testgraph, grid, and city. Each of those contains the runfiles, model files, and HPC results.


  • DirectPolicySearch
  • testgraph
  • results_HPC/
  • fugitive_interception_model_city.py
  • run_city.py
  • run_experiment_city.py
  • grid
  • city
  • OneShot
  • PeriodicReOpt
  • PolicyTreeOpt


The optimization algorithm can be found under platypus-fork.zip.

history
  • 2024-11-25 first online, published, posted
publisher
4TU.ResearchData
format
.py, .ipynb, .csv, .pkl, .graphml
associated peer-reviewed publication
Timely Adaptive Strategies for Fugitive Interception
funding
  • National Police Artificial Intelligence Lab National Police Artificial Intelligence Lab
organizations
TU Delft, Faculty of Technology, Policy and Management, Department of Multi-Actor Systems (MAS)

DATA

files (2)