论文标题
网络挖掘以告知电动汽车充电站的位置
Web Mining to Inform Locations of Charging Stations for Electric Vehicles
论文作者
论文摘要
充电站的可用性是促进电动汽车（EV）作为碳友好方式运输方式的重要因素。因此，对于城市规划师来说，关键的问题是在哪里放置充电站，以便它们达到大量利用。在这里，我们假设EV充电站的利用是由与利益点（POI）的接近（POI）驱动的，因为电动汽车所有者在充电站和POI之间有一定的有限的意愿。为了解决我们的研究问题，我们提出了Web挖掘的使用：我们表征了OpenStreetMap不同POI对充电站利用的影响。为此，我们提出了一个量身定制的可解释模型，该模型考虑了POI和充电站的完整空间分布。然后，这使我们能够估计不同POI类型的影响的距离和幅度。我们通过大约数据来评估我们的模型。荷兰阿姆斯特丹的300个充电站和4,000个POI。我们的模型比最先进的基线实现了优越的性能，最重要的是，能够提供无与伦比的解释性水平。据我们所知，以前的论文没有通过估计POI相关的空间邻近性来量化POI对充电站利用的影响。因此，我们的发现可以帮助城市规划人员确定充电站的有效位置。
The availability of charging stations is an important factor for promoting electric vehicles (EVs) as a carbon-friendly way of transportation. Hence, for city planners, the crucial question is where to place charging stations so that they reach a large utilization. Here, we hypothesize that the utilization of EV charging stations is driven by the proximity to points-of-interest (POIs), as EV owners have a certain limited willingness to walk between charging stations and POIs. To address our research question, we propose the use of web mining: we characterize the influence of different POIs from OpenStreetMap on the utilization of charging stations. For this, we present a tailored interpretable model that takes into account the full spatial distributions of both the POIs and the charging stations. This allows us then to estimate the distance and magnitude of the influence of different POI types. We evaluate our model with data from approx. 300 charging stations and 4,000 POIs in Amsterdam, Netherlands. Our model achieves a superior performance over state-of-the-art baselines and, on top of that, is able to offer an unmatched level of interpretability. To the best of our knowledge, no previous paper has quantified the POI influence on charging station utilization from real-world usage data by estimating the spatial proximity in which POIs are relevant. As such, our findings help city planners in identifying effective locations for charging stations.
