TY - JOUR
T1 - Intention-Aware Routing of Electric Vehicles
AU - de Weerdt, Mathijs
AU - Stein, Sebastian
AU - Gerding, Enrico H.
AU - Robu, Valentin
AU - Jennings, Nicholas R.
N1 - "This work was carried out as part of the EPSRC-funded ORCHID project (EP/I011587/1) and the industry-funded IDEaS project."
PY - 2016/5
Y1 - 2016/5
N2 - This paper introduces a novel intention-aware routing system (IARS) for electric vehicles. This system enables vehicles to compute a routing policy that minimises 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 to 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 queueing times at the 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 minimises 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 to 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 queueing times at the stations and a more than 50% reduction in overall journey times.
UR - http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7365482
U2 - 10.1109/TITS.2015.2506900
DO - 10.1109/TITS.2015.2506900
M3 - Article
SN - 1524-9050
VL - 17
SP - 1472
EP - 1482
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 5
ER -