Previous methods for passenger demand inference are unable to capture the effect of all possible random factors (e.g., accident and weather), hence resulting in insufficient accuracy. Moreover, due to the lack of charging optimization, existing taxicab dispatching methods cannot be applied to electric taxicabs directly. We propose CD-Guide, which provides Charging and Dispatching Guide for electric taxicabs based on customized selection and training of historical passenger demand data, multiobjective optimization, and reinforcement learning (RL). By analyzing a large-scale electric taxicab data set, we found that: 1) the histogram of passengers’ origin buildings is effective in illustrating the suitability of historical data for learning; 2) passenger demands in different regions vary a lot due to various random factors; and 3) charging time must be considered in dispatching electric taxicabs. We first develop a passenger demand inference model based on customized selection and training of suitable historical passenger demand data. Then, we develop two taxicab guidance methods that utilize multiobjective optimization and RL, respectively, to maximize the taxicab’s likelihood of finding passengers, maximally prevent the taxicab from missing passengers due to charging, and, meanwhile, maintain the continuous service of the taxicab. Extensive experiments on real-world data sets demonstrate that compared with the state of the art, CD-Guide increases the total number of served passengers by 100%, and the minimum State-of-Charge of all taxicabs by 75% during all time slots.

中文翻译：

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光盘指南：电动出租车调度与充电方法


以往的乘客需求推断方法无法捕获所有可能的随机因素（例如事故和天气）的影响，因此导致准确性不足。而且，由于缺乏充电优化，现有的出租车调度方法无法直接应用于电动出租车。我们提出CD-Guide，它基于历史乘客需求数据的定制选择和训练、多目标优化和强化学习（RL），为电动出租车提供充电和调度指南。通过分析大规模电动出租车数据集，我们发现：1）乘客始发建筑物的直方图可以有效说明历史数据是否适合学习； 2）受各种随机因素影响，不同地区的旅客需求存在较大差异； 3）调度电动出租车必须考虑充电时间。我们首先开发基于合适的历史乘客需求数据的定制选择和训练的乘客需求推断模型。然后，我们开发了两种出租车引导方法，分别利用多目标优化和强化学习，最大限度地提高出租车找到乘客的可能性，最大限度地防止出租车因收费而漏掉乘客，同时保持出租车的连续服务。对真实世界数据集的大量实验表明，与最先进的技术相比，CD-Guide 将服务的乘客总数增加了 100%，并且所有出租车在所有时段的最低收费状态增加了 75% 。