Computing the shortest path between two vertices is a fundamental problem in road networks. Most of the existing works assume that the edges in the road networks have no labels, but in many real applications, the edges have labels and label constraints may be placed on the edges appearing on a valid shortest path. Hence, we study the label-constrained shortest path queries in this paper. In order to process such queries efficiently, we adopt an index-based approach and propose a novel index structure, \(\textsf{LSD}\)-\(\textsf{Index}\), based on tree decomposition. With \(\textsf{LSD}\)-\(\textsf{Index}\), we design efficient query processing and index construction algorithms with good performance guarantees. Moreover, due to the dynamic properties of real-world networks, we also devise index maintenance algorithms that can maintain the index efficiently. To evaluate the performance of proposed methods, we conduct extensive experimental studies using large real road networks including the whole USA road network. Compared with the state-of-the-art approach, the experimental results demonstrate that our algorithm not only achieves up to two orders of magnitude speedup in query processing time but also consumes much less index space. Meanwhile, the experimental results also show that the index can also be efficiently constructed and maintained for dynamic graphs.

计算两个顶点之间的最短路径是道路网络中的一个基本问题。大多数现有工作假设道路网络中的边没有标签，但在许多实际应用中，边有标签，并且标签约束可以放置在有效最短路径上出现的边上。因此，我们在本文中研究标签约束的最短路径查询。为了有效地处理此类查询，我们采用基于索引的方法，并提出了一种基于树分解的新颖索引结构\(\textsf{LSD}\) - \(\textsf{Index}\)。利用\(\textsf{LSD}\) - \(\textsf{Index}\)，我们设计了高效的查询处理和索引构建算法，并具有良好的性能保证。此外，由于现实世界网络的动态特性，我们还设计了可以有效维护索引的索引维护算法。为了评估所提出方法的性能，我们使用大型真实道路网络（包括整个美国道路网络）进行了广泛的实验研究。与最先进的方法相比，实验结果表明，我们的算法不仅在查询处理时间上实现了两个数量级的加速，而且消耗的索引空间也少得多。同时，实验结果还表明，对于动态图，该索引也可以高效地构建和维护。