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A route pruning algorithm for an automated geographic location graph construction
Automated construction of location graphs is instrumental but challenging, particularly in logistics optimisation problems and agent-based movement simulations. Hence, we propose an algorithm for automated construction of location graphs, in which vertices correspond to geographic locations of inter...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8172915/ https://www.ncbi.nlm.nih.gov/pubmed/34078986 http://dx.doi.org/10.1038/s41598-021-90943-8 |
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author | Schweimer, Christoph Geiger, Bernhard C. Wang, Meizhu Gogolenko, Sergiy Mahmood, Imran Jahani, Alireza Suleimenova, Diana Groen, Derek |
author_facet | Schweimer, Christoph Geiger, Bernhard C. Wang, Meizhu Gogolenko, Sergiy Mahmood, Imran Jahani, Alireza Suleimenova, Diana Groen, Derek |
author_sort | Schweimer, Christoph |
collection | PubMed |
description | Automated construction of location graphs is instrumental but challenging, particularly in logistics optimisation problems and agent-based movement simulations. Hence, we propose an algorithm for automated construction of location graphs, in which vertices correspond to geographic locations of interest and edges to direct travelling routes between them. Our approach involves two steps. In the first step, we use a routing service to compute distances between all pairs of L locations, resulting in a complete graph. In the second step, we prune this graph by removing edges corresponding to indirect routes, identified using the triangle inequality. The computational complexity of this second step is [Formula: see text] , which enables the computation of location graphs for all towns and cities on the road network of an entire continent. To illustrate the utility of our algorithm in an application, we constructed location graphs for four regions of different size and road infrastructures and compared them to manually created ground truths. Our algorithm simultaneously achieved precision and recall values around 0.9 for a wide range of the single hyperparameter, suggesting that it is a valid approach to create large location graphs for which a manual creation is infeasible. |
format | Online Article Text |
id | pubmed-8172915 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-81729152021-06-04 A route pruning algorithm for an automated geographic location graph construction Schweimer, Christoph Geiger, Bernhard C. Wang, Meizhu Gogolenko, Sergiy Mahmood, Imran Jahani, Alireza Suleimenova, Diana Groen, Derek Sci Rep Article Automated construction of location graphs is instrumental but challenging, particularly in logistics optimisation problems and agent-based movement simulations. Hence, we propose an algorithm for automated construction of location graphs, in which vertices correspond to geographic locations of interest and edges to direct travelling routes between them. Our approach involves two steps. In the first step, we use a routing service to compute distances between all pairs of L locations, resulting in a complete graph. In the second step, we prune this graph by removing edges corresponding to indirect routes, identified using the triangle inequality. The computational complexity of this second step is [Formula: see text] , which enables the computation of location graphs for all towns and cities on the road network of an entire continent. To illustrate the utility of our algorithm in an application, we constructed location graphs for four regions of different size and road infrastructures and compared them to manually created ground truths. Our algorithm simultaneously achieved precision and recall values around 0.9 for a wide range of the single hyperparameter, suggesting that it is a valid approach to create large location graphs for which a manual creation is infeasible. Nature Publishing Group UK 2021-06-02 /pmc/articles/PMC8172915/ /pubmed/34078986 http://dx.doi.org/10.1038/s41598-021-90943-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Schweimer, Christoph Geiger, Bernhard C. Wang, Meizhu Gogolenko, Sergiy Mahmood, Imran Jahani, Alireza Suleimenova, Diana Groen, Derek A route pruning algorithm for an automated geographic location graph construction |
title | A route pruning algorithm for an automated geographic location graph construction |
title_full | A route pruning algorithm for an automated geographic location graph construction |
title_fullStr | A route pruning algorithm for an automated geographic location graph construction |
title_full_unstemmed | A route pruning algorithm for an automated geographic location graph construction |
title_short | A route pruning algorithm for an automated geographic location graph construction |
title_sort | route pruning algorithm for an automated geographic location graph construction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8172915/ https://www.ncbi.nlm.nih.gov/pubmed/34078986 http://dx.doi.org/10.1038/s41598-021-90943-8 |
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