Automated generalization of road network data is of great concern to the map generalization community because of the importance of road data and the difficulty involved. Complex junctions are where roads meet and join in a complicated way and identifying them is a key issue in road network generalization. In addition to their structural complexity, complex junctions don’t have regular geometric boundary and their representation in spatial data is scale-dependent. All these together make them hard to identify. Existing methods use geometric and topological statistics to characterize and identify them, and are thus error-prone, scale-dependent and lack generality. More significantly, they cannot ensure the integrity of complex junctions. This study overcomes the obstacles by clarifying the topological boundary of a complex junction, which provides the basis for straightforward identification of them. Test results show the proposed method can find and isolate complex junctions in a road network with their integrity and is able to handle different road representations. The integral identification achieved can help to guarantee connectivity among roads when simplifying complex junctions, and greatly facilitate the geometric and semantic simplification of them.

中文翻译：

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识别路网中的复杂路口

由于道路数据的重要性和所涉及的困难，道路网络数据的自动归纳是地图归纳界非常关注的问题。复杂的交叉路口是道路以复杂的方式汇合和汇合的地方，识别它们是道路网络泛化的关键问题。除了其结构复杂性之外，复杂的交汇处没有规则的几何边界，并且它们在空间数据中的表示方式还取决于比例。所有这些共同使它们难以识别。现有方法使用几何和拓扑统计来表征和识别它们，因此容易出错，与比例有关且缺乏通用性。更重要的是，它们不能确保复杂连接的完整性。这项研究通过阐明复杂交界处的拓扑边界来克服障碍，这为直接识别它们提供了基础。测试结果表明，所提出的方法可以发现并隔离路网中的复杂路口，并具有完整性，并能够处理不同的道路表示形式。当简化复杂的路口时，实现的整体识别可以帮助确保道路之间的连通性，并极大地促进了道路的几何和语义简化。