Shortest Path Queries On Road Networks Based On Pre-Computation Techniques

Computing the shortest path between two given locations in a road network is an important problem that finds applications in various map services and commercial nav-igation products. The state-of-the-art solutions for the problem can be divided into two categories:spatial-coherence-based methods and vertex-importance-based approaches, which both are based on pre-computation techniques. The two categories of techniques, however, have not been compared systematically under the same experimental frame-work, as they were developed from two independent lines of research that do not refer to each other. This renders it difficult for a practitioner to decide which technique should be adopted for a specific application. Furthermore, the experimental evaluation of the existing techniques, as presented in previous work, falls short in several aspects. Some methods were tested only on small road networks with up to one hundred thousand ver-tices; some approaches were evaluated using distance queries (instead of shortest path queries), namely, queries that ask only for the length of the shortest path; a state-of-the-art technique was examined based on a faulty implementation that led to incorrect query results.To address the above issues, this thesis presents a comprehensive comparison of the most advanced spatial-coherence-based and vertex-importance-based approaches both theoretically and empirically, and proposes a new index structure Shortest Path B+ tree to encode the shortest path information in a concise way under the framework of spatial-coherence-based methods, which supplements the existing work. Using a variety of real road networks with up to twenty million vertices, we evaluated each technique in terms of its preprocessing time, space consumption, and query efficiency (for both shortest path and distance queries). Our experimental results reveal the characteristics of different techniques, based on which we provide guidelines on selecting appropriate methods for various scenarios.