Standard

Intention-Aware Routing of Electric Vehicles. / de Weerdt, M.M.; Stein, Sebastian; Gerding, Enrico; Robu, Valentin; Jennings, Nick.

In: IEEE Transactions on Intelligent Transportation Systems, Vol. 17, No. 5, 24.12.2015, p. 1472 - 1482.

Research output: Scientific - peer-reviewArticle

Harvard

de Weerdt, MM, Stein, S, Gerding, E, Robu, V & Jennings, N 2015, 'Intention-Aware Routing of Electric Vehicles' IEEE Transactions on Intelligent Transportation Systems, vol 17, no. 5, pp. 1472 - 1482. DOI: 10.1109/TITS.2015.2506900

APA

de Weerdt, M. M., Stein, S., Gerding, E., Robu, V., & Jennings, N. (2015). Intention-Aware Routing of Electric Vehicles. IEEE Transactions on Intelligent Transportation Systems, 17(5), 1472 - 1482. DOI: 10.1109/TITS.2015.2506900

Vancouver

de Weerdt MM, Stein S, Gerding E, Robu V, Jennings N. Intention-Aware Routing of Electric Vehicles. IEEE Transactions on Intelligent Transportation Systems. 2015 Dec 24;17(5):1472 - 1482. Available from, DOI: 10.1109/TITS.2015.2506900

Author

de Weerdt, M.M. ; Stein, Sebastian ; Gerding, Enrico ; Robu, Valentin ; Jennings, Nick. / Intention-Aware Routing of Electric Vehicles. In: IEEE Transactions on Intelligent Transportation Systems. 2015 ; Vol. 17, No. 5. pp. 1472 - 1482

BibTeX

@article{1691595817f74791b3335c5e07fef081,
title = "Intention-Aware Routing of Electric Vehicles",
abstract = "This paper introduces a novel intention-aware routing system (IARS) for electric vehicles. This system enables vehicles to compute a routing policy that minimizes their expected journey time while considering the policies, or intentions, of other vehicles. Considering such intentions is critical for electric vehicles, which may need to recharge en route and face potentially significant queueing times if other vehicles choose the same charging stations. To address this, the computed routing policy takes into consideration predicted queueing times at the stations, which are derived from the current intentions of other electric vehicles. The efficacy of IARS is demonstrated through simulations using realistic settings based on real data from The Netherlands, including charging station locations, road networks, historical travel times, and journey origin-destination pairs. In these settings, IARS is compared with a number of state-of-the-art benchmark routing algorithms and achieves significantly lower average journey times. In some cases, IARS leads to an over 80% improvement in waiting times at charging stations and a more than 50% reduction in overall journey times.",
keywords = "multi-agent systems, intelligent vehicles, vehicle routing, traffic control, electric vehicles, decision making",
author = "{de Weerdt}, M.M. and Sebastian Stein and Enrico Gerding and Valentin Robu and Nick Jennings",
year = "2015",
month = "12",
doi = "10.1109/TITS.2015.2506900",
volume = "17",
pages = "1472 -- 1482",
journal = "IEEE Transactions on Intelligent Transportation Systems",
issn = "1524-9050",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "5",

}

RIS

TY - JOUR

T1 - Intention-Aware Routing of Electric Vehicles

AU - de Weerdt,M.M.

AU - Stein,Sebastian

AU - Gerding,Enrico

AU - Robu,Valentin

AU - Jennings,Nick

PY - 2015/12/24

Y1 - 2015/12/24

N2 - This paper introduces a novel intention-aware routing system (IARS) for electric vehicles. This system enables vehicles to compute a routing policy that minimizes their expected journey time while considering the policies, or intentions, of other vehicles. Considering such intentions is critical for electric vehicles, which may need to recharge en route and face potentially significant queueing times if other vehicles choose the same charging stations. To address this, the computed routing policy takes into consideration predicted queueing times at the stations, which are derived from the current intentions of other electric vehicles. The efficacy of IARS is demonstrated through simulations using realistic settings based on real data from The Netherlands, including charging station locations, road networks, historical travel times, and journey origin-destination pairs. In these settings, IARS is compared with a number of state-of-the-art benchmark routing algorithms and achieves significantly lower average journey times. In some cases, IARS leads to an over 80% improvement in waiting times at charging stations and a more than 50% reduction in overall journey times.

AB - This paper introduces a novel intention-aware routing system (IARS) for electric vehicles. This system enables vehicles to compute a routing policy that minimizes their expected journey time while considering the policies, or intentions, of other vehicles. Considering such intentions is critical for electric vehicles, which may need to recharge en route and face potentially significant queueing times if other vehicles choose the same charging stations. To address this, the computed routing policy takes into consideration predicted queueing times at the stations, which are derived from the current intentions of other electric vehicles. The efficacy of IARS is demonstrated through simulations using realistic settings based on real data from The Netherlands, including charging station locations, road networks, historical travel times, and journey origin-destination pairs. In these settings, IARS is compared with a number of state-of-the-art benchmark routing algorithms and achieves significantly lower average journey times. In some cases, IARS leads to an over 80% improvement in waiting times at charging stations and a more than 50% reduction in overall journey times.

KW - multi-agent systems

KW - intelligent vehicles

KW - vehicle routing

KW - traffic control

KW - electric vehicles

KW - decision making

U2 - 10.1109/TITS.2015.2506900

DO - 10.1109/TITS.2015.2506900

M3 - Article

VL - 17

SP - 1472

EP - 1482

JO - IEEE Transactions on Intelligent Transportation Systems

T2 - IEEE Transactions on Intelligent Transportation Systems

JF - IEEE Transactions on Intelligent Transportation Systems

SN - 1524-9050

IS - 5

ER -

ID: 10329498