{"type":"ScholarlyArticle","title":"Intention-Aware Routing to Minimise Delays at Electric Vehicle Charging Stations","authors":[{"name":"Mathijs de Weerdt","slug":"mathijs-de-weerdt","given":"Mathijs","family":"de Weerdt","affiliation":""},{"name":"Enrico H. Gerding","slug":"enrico-h-gerding","given":"Enrico H.","family":"Gerding","affiliation":""},{"name":"Sebastian Stein","slug":"sebastian-stein","given":"Sebastian","family":"Stein","affiliation":""},{"name":"Valentin Robu","slug":"valentin-robu","given":"Valentin","family":"Robu","affiliation":""},{"name":"Nicholas R. Jennings","slug":"nicholas-r-jennings","given":"Nicholas R.","family":"Jennings","affiliation":""}],"abstract":"En-route charging stations allow electric vehicles to greatly extend their range. However, as a full charge takes a considerable amount of time, there may be significant waiting times at peak hours.To address this problem, we propose a novel navigation system, which communicates its intentions (i.e., routing policies) to other drivers. Using these intentions, our system accurately predicts congestion at charging stations and suggests the most efficient route to its user. We achieve this by extending existing time-dependent stochastic routing algorithms to include the battery's state of charge and charging stations. Furthermore, we describe a novel technique for combining historical information with agent intentions to predict the queues at charging stations. Through simulations we show that our system leads to a significant increase in utility compared to existing approaches that do not explicitly model waiting times or use intentions, in some cases reducing waiting times by over 80% and achieving near-optimal overall journey times.","doi":null,"url":"https://ijcai.org/proceedings/2013/24","pdf":"https://ijcai.org/proceedings/2013/0024.pdf","markdown":"https://ijcai.org/proceedings/2013/24.md","bibtex":"https://ijcai.org/proceedings/2013/bibtex/24","volume":{"slug":"2013","title":"Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2013","url":"https://ijcai.org/proceedings/2013/","year":2013,"location":"Beijing, China","dates":"3–9 August 2013","editors":"Francesca Rossi","isbn":"978-1-57735-633-2","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":24,"pages":{"first":83,"last":88},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}