Label-constrained shortest path query processing on road networks

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.