电动汽车充电站选址利用地理空间人工智能（GeoAI）
Electric Vehicle Charging Station Location Selection using Geospatial Artificial Intelligence (GeoAI)
- Kyungil University（庆一大学）
- Korea University（高丽大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出一种基于GeoAI的框架，结合VAE和GCN，利用多源地理空间数据预测电动汽车充电站选址，在美国真实数据上取得0.87的F1分数，并识别出27个新候选位置，为基础设施规划提供见解。
AI中文摘要：
随着电动汽车（EV）普及率的提高，确保高效且分布合理的充电基础设施已成为一项关键挑战。尽管许多电动汽车充电站选址问题（CSLP）的研究侧重于最小化成本或行驶距离，但考虑现有站点周围影响运营性能的地理空间特征至关重要。本研究提出了一种基于地理空间人工智能（GeoAI）的框架，该框架整合了高维度的电动汽车相关地理空间数据，包括电动汽车使用情况、土地利用、人口和交通属性。我们在模型中融入了变分自编码器（VAE）和图卷积网络（GCN），以捕捉现有充电站之间的相似性，并识别未来站点的合适位置。VAE将高维电动汽车输入数据压缩到低维潜在空间，GCN利用该潜在表示来预测适合建设充电站的位置。利用美国德克萨斯州布莱恩-大学城（Bryan-College Station）的真实数据，所提出的模型优于最先进的基线模型，在区分现有站点位置与非站点位置方面取得了0.87的F1分数。该模型还基于相似性评分，识别出另外27个与现有站点地理空间特征相似的候选位置。我们进一步评估了两种政策实施场景，即最大化地理空间相似性和最小化总行驶距离，每种场景都产生了与不同战略目标一致的不同结果。研究结果凸显了将空间背景纳入CSLP的重要性，并为未来电动汽车基础设施规划提供了宝贵见解，促进了快速增长的电动出行领域中的效率与可达性。
英文摘要：
As electric vehicle (EV) adoption increases, ensuring efficient and well-distributed charging infrastructure has become a critical challenge. While many EV charging station location problem (CSLP) studies focus on minimizing costs or travel distance, it is crucial to consider the surrounding geospatial characteristics of existing stations that influence operational performance. This study proposes a geospatial artificial intelligence (GeoAI)-based framework that integrates high-dimensional EV-related geospatial data, including EV usage, land-use, population, and traffic attributes. We incorporate a variational autoencoder (VAE) and a graph convolutional network (GCN) into the model to capture similarities among existing charging stations, and to identify suitable locations for future stations. The VAE compresses high-dimensional EV input data into a low-dimensional latent space, and the GCN uses this latent representation to predict locations suitable for charging stations. Using real-world data from Bryan-College Station, Texas, US, the proposed model outperforms state-of-the-art baselines, achieving an F1-score of 0.87 in distinguishing existing station locations from non-station locations. The model also identifies 27 additional candidate locations that show geospatial characteristics similar to those of existing stations, based on a similarity score. We further evaluate two policy implementation scenarios, maximizing geospatial similarity and minimizing total travel distance, each yielding different outcomes aligned with distinct strategic objectives. The findings highlight the importance of incorporating spatial context into CSLP and provide valuable insights for future EV infrastructure planning, promoting both efficiency and accessibility in the rapidly growing electric mobility sector.
