Research on Optimizing Top-k Route Queries Based on Pruning by Moving Distance

In navigation planning, users usually prioritize routes that meet their personal-ized needs, considering factors such as path length, path cost, and coverage of points of interest. Multi-keyword top-k optimal route query (k-ORCSK) is a type of query used to plan such routes. However, as the scale of road networks continues to expand, the number of nodes and edges that need to be processed increases significantly. Additionally, considering different combinations of query keywords leads to increased complexity in the query process as the number of keywords grows. To address these limitations, an optimal query algorithm named the Moving Distance Pruning Algorithm (MDPA) is proposed. The MDPA algorithm initially decomposes the complex road network into multiple parts, reducing memory overhead by storing the path weights within each part. It then employs strategies such as moving distance pruning and global priority to prune candidate paths, optimizing existing algorithms. Experiments on several real road networks demonstrate that the MDPA algorithm can more effectively provide higher quality results and exhibits good scalability. The proposal and optimization of the MDPA algorithm offer an effective approach for handling large-scale road networks and complex query tasks, providing a more reliable and efficient solution for personalized navigation planning.