Fast travel-distance estimation using overhead graph
Shortest path can be computed in real-time for single navigational instructions. However, in complex optimisation tasks, lots of travel-distances (lengths of shortest paths) are needed and the total workload becomes a burden. We present a fast method for estimating the travel-distance from one locat...
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Informa UK Limited
2021
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Online Access: | https://erepo.uef.fi/handle/123456789/26390 |
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ftuniveasternfin:oai:erepo.uef.fi:123456789/26390 2023-05-15T17:00:19+02:00 Fast travel-distance estimation using overhead graph Mariescu-Istodor, Radu Fränti, Pasi School of Computing, activities 2021-11-08T07:26:11Z 261-279 https://erepo.uef.fi/handle/123456789/26390 englanti unknown Informa UK Limited Journal of location based services http://dx.doi.org/10.1080/17489725.2021.1889058 10.1080/17489725.2021.1889058 1748-9725 4 15 https://erepo.uef.fi/handle/123456789/26390 CC BY 4.0 openAccess © 2021 The Authors https://creativecommons.org/licenses/by/4.0/ CC-BY travel-time estimation travel-distance estimation road network graph Tieteelliset aikakauslehtiartikkelit A1 article artikkeli 2021 ftuniveasternfin https://doi.org/10.1080/17489725.2021.1889058 2022-12-11T06:55:05Z Shortest path can be computed in real-time for single navigational instructions. However, in complex optimisation tasks, lots of travel-distances (lengths of shortest paths) are needed and the total workload becomes a burden. We present a fast method for estimating the travel-distance from one location to another by using an overhead graph that stores the ratio between the bird-distance and the travel-distance for few representative locations. The travel-distance is then estimated for any two locations using the corresponding value between their nearest nodes in the graph. We test the method within an optimization setting where the goal is to relocate health services so that the travel-distance of patients is minimised. We build the overhead graph using road network information from Open Street Map and experiment with real-world data in the region of North Karelia, Finland as a part of the ongoing IMPRO project. The results show that the average estimation error is 0.5 km with a graph of 512 nodes, and the total processing time reduces from 1.2 hours to 2.9 seconds per iteration in the optimisation process. The error in the estimated travel-distances is 2%, on average, which is significantly smaller than 8% of the second best estimation method. published version peerReviewed Article in Journal/Newspaper karelia* UEF eRepository (University of Eastern Finland) Journal of Location Based Services 15 4 261 279 |
institution |
Open Polar |
collection |
UEF eRepository (University of Eastern Finland) |
op_collection_id |
ftuniveasternfin |
language |
unknown |
topic |
travel-time estimation travel-distance estimation road network graph |
spellingShingle |
travel-time estimation travel-distance estimation road network graph Mariescu-Istodor, Radu Fränti, Pasi Fast travel-distance estimation using overhead graph |
topic_facet |
travel-time estimation travel-distance estimation road network graph |
description |
Shortest path can be computed in real-time for single navigational instructions. However, in complex optimisation tasks, lots of travel-distances (lengths of shortest paths) are needed and the total workload becomes a burden. We present a fast method for estimating the travel-distance from one location to another by using an overhead graph that stores the ratio between the bird-distance and the travel-distance for few representative locations. The travel-distance is then estimated for any two locations using the corresponding value between their nearest nodes in the graph. We test the method within an optimization setting where the goal is to relocate health services so that the travel-distance of patients is minimised. We build the overhead graph using road network information from Open Street Map and experiment with real-world data in the region of North Karelia, Finland as a part of the ongoing IMPRO project. The results show that the average estimation error is 0.5 km with a graph of 512 nodes, and the total processing time reduces from 1.2 hours to 2.9 seconds per iteration in the optimisation process. The error in the estimated travel-distances is 2%, on average, which is significantly smaller than 8% of the second best estimation method. published version peerReviewed |
author2 |
School of Computing, activities |
format |
Article in Journal/Newspaper |
author |
Mariescu-Istodor, Radu Fränti, Pasi |
author_facet |
Mariescu-Istodor, Radu Fränti, Pasi |
author_sort |
Mariescu-Istodor, Radu |
title |
Fast travel-distance estimation using overhead graph |
title_short |
Fast travel-distance estimation using overhead graph |
title_full |
Fast travel-distance estimation using overhead graph |
title_fullStr |
Fast travel-distance estimation using overhead graph |
title_full_unstemmed |
Fast travel-distance estimation using overhead graph |
title_sort |
fast travel-distance estimation using overhead graph |
publisher |
Informa UK Limited |
publishDate |
2021 |
url |
https://erepo.uef.fi/handle/123456789/26390 |
genre |
karelia* |
genre_facet |
karelia* |
op_relation |
Journal of location based services http://dx.doi.org/10.1080/17489725.2021.1889058 10.1080/17489725.2021.1889058 1748-9725 4 15 https://erepo.uef.fi/handle/123456789/26390 |
op_rights |
CC BY 4.0 openAccess © 2021 The Authors https://creativecommons.org/licenses/by/4.0/ |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1080/17489725.2021.1889058 |
container_title |
Journal of Location Based Services |
container_volume |
15 |
container_issue |
4 |
container_start_page |
261 |
op_container_end_page |
279 |
_version_ |
1766052962247901184 |