随着城市化进程的快速推进，居民出行与公交系统的供需矛盾日益突出，公交系统覆盖外的“盲点”问题越来越普遍，严重影响了居民出行与城市交通。然而，“盲点”的识别问题却一直是一大难题。传统以经验为主的识别方法难以描述“盲点”多时相，不定中心，模糊边界的特征。为此，本项目拟借助点过程理论对公交系统“盲点”明确定义，通过对传统点过程聚类算法中的丛集判定以及模式提取方法改进，提出一个基于出租车GPS轨迹数据识别城市公交系统“盲点”的方法，对公交系统的站点“盲点”、线路“盲点”的中心位置以及空间范围进行识别。研究以北京市为案例，分析了北京市公交系统“盲点”的分布规律与成因机制，从而为城市公共交通优化决策提供依据。

Blind spots in public transport system are widely appeared in the modern cities, as fast growth of urbanization being out of step with public transport promotion. The uncovered areas of public stations are attribute to the lack of transportation infrastructure, while the inconvenient bus lines are inevitable consequence of flawed transportation planning. These two types of blind spots in public transport system seriously influence the daily commute and other trips. However, way to identify the extents of blind spots are inefficient because the residential trips are changing every day. This project intend to give a definition of blind spot in public transport system from point process theory and present a new method to identify two types of blind spots in public transport system. The classic density-based algorithm will be improved with new definitions of core point and cluster to solve the clustering problem under constraint of incomplete road network. Performance of this method will be assessed by comparing simulated data and clustering result. In other hand, we take Beijing as a case study and analyze spatial distributions and mechanisms of the blind spots, so as to provide a basis for optimization of urban public transport.