Task Assignment Framework for Online Car-Hailing Systems With Electric Vehicles

Recently, transportation-as-a-service (TaaS) becomes an increasing trend, and online taxi platforms start to apply electric vehicles to serve passengers. Since the recharging time of an electric vehicle is long and non-negligible, it is necessary to smartly arrange the recharging schedules of electric vehicles in working schedules. In order to maximize the number of served taxi-calling tasks, online taxi platforms assign electric vehicles whose remaining electric power is enough to serve the dynamically arriving taxi-calling tasks and schedule suitable idle vehicles to recharging piles to recharge. We formally define the power-aware electric vehicle assignment (PAEVA) problem to serve as many taxi-calling tasks as possible under the constraints of remaining electric power and deadline. We prove that the PAEVA problem is NP-hard. To solve PAEVA, we design a novel strategy to help arrange the schedules of electric vehicles. Specifically, the strategy requires that, in a time slot and an area gird, the ratio of the number of electric vehicles whose remaining electric power is higher than a threshold
$\alpha$
to the number of predicted taxi-calling tasks should be higher than a threshold
$\beta$
. We propose two approximation approaches with theoretical guarantees to adaptively determine the values of the two thresholds of the strategy. We evaluate our solutions' effectiveness and efficiency by comprehensive experiments on real datasets.