The various performances of buses at stations bring lots of difficulties for operators to manage them to improve the service quality. This paper proposes a data-driven framework to analyze the patterns of stations with network structure data, points of interest (POI) data and vehicle global positioning system (GPS) trajectory data. First, we build six indicators based on these data to measure the performance from station perspective. The results show that the number of POI around stations within 1 kilometer follows an exponential distribution. Moreover, the average headway and headway deviation of stations follow lognormal distributions. Second, we use agglomerative hierarchical clustering method to divided bus stations into different groups. Results indicate that the bus stations of Jinan could be divided into four groups with obvious characteristics. The findings could help operators to make exclusive strategies to manage bus systems.

车站公交车的各种性能给运营商管理带来很大的困难，以提高服务质量。本文提出了一种数据驱动框架，以分析具有网络结构数据，兴趣点（POI）数据和车辆全球定位系统（GPS）轨迹数据的车站的模式。首先，我们基于这些数据构建六个指标，以从站点角度衡量绩效。结果表明，在1公里以内的站点周围的POI数量呈指数分布。此外，车站的平均时距和时距偏差遵循对数正态分布。其次，我们使用聚集层次聚类方法将公交车站分为不同的组。结果表明，济南市公交车站可分为特征明显的四类。这些发现可以帮助运营商制定独家策略来管理总线系统。