{"type":"ScholarlyArticle","title":"Scalable Ultrafast Almost-optimal Euclidean Shortest Paths","authors":[{"name":"Stefan Funke","slug":"stefan-funke","given":"Stefan","family":"Funke","affiliation":"University of Stuttgart , Germany"},{"name":"Daniel Koch","slug":"daniel-koch","given":"Daniel","family":"Koch","affiliation":"University of Stuttgart , Germany"},{"name":"Claudius Proissl","slug":"claudius-proissl","given":"Claudius","family":"Proissl","affiliation":"University of Stuttgart , Germany"},{"name":"Axel Schneewind","slug":"axel-schneewind","given":"Axel","family":"Schneewind","affiliation":"University of Stuttgart , Germany"},{"name":"Armin Weiß","slug":"armin-wei","given":"Armin","family":"Weiß","affiliation":"University of Stuttgart , Germany"},{"name":"Felix Weitbrecht","slug":"felix-weitbrecht","given":"Felix","family":"Weitbrecht","affiliation":"University of Stuttgart , Germany"}],"abstract":"We consider the problem of computing high-quality Euclidean shortest paths amidst obstacles on a large scale. By transferring and adapting speed-up techniques from the road network setting, we are able to compute source target paths for problem instances with several million obstacle vertices within few milliseconds after moderate preprocessing. We show experimentally that for small instances where optimal solutions are easily available on average our computed paths are less than 0.3% longer than the optimum. For large instances a new lower-bounding technique shows that on average our computed paths are less than 2% longer than the optimum paths. We compare our approach with the current state-of-the-art on problem instances derived from the OpenStreetMap project.","doi":"10.24963/ijcai.2024/742","url":"https://ijcai.org/proceedings/2024/742","pdf":"https://ijcai.org/proceedings/2024/0742.pdf","markdown":"https://ijcai.org/proceedings/2024/742.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/742","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Planning and Scheduling","track":"Main Track","number":742,"pages":{"first":6716,"last":6723},"keywords":["Planning and Scheduling: PS: Routing","Planning and Scheduling: PS: Applications","Search: S: Combinatorial search and optimisation","Multidisciplinary Topics and Applications: MTA: Transportation"],"videos":[],"references":[{"position":1,"raw":"[Aleksandrov et al., 2000] Lyudmil Aleksandrov, Anil Ma- heshwari, and Jörg-Rüdiger Sack. 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