A Learning-based Method for Computing Shortest Path Distances on Road Networks
Shuai Huang, Yong Wang, Tianyu Zhao, Guoliang Li
Abstract
Computing the shortest path distances between two vertices on road networks is a core operation in many real-world applications, e.g., finding the closest taxi/hotel. However existing techniques have several limitations. First, traditional Dijkstra-based methods have long latency and cannot meet the high-performance requirement. Second, existing indexing-based methods either involve huge index sizes or have poor performance. To address these limitations, in this paper we propose a learning-based method which can efficiently compute an approximate shortest-path distance such that (1) the performance is super fast, e.g., taking 60-150 nanoseconds; (2) the error ratio of the approximate results is super small, e.g., below 0.7%; (3) scales well to large road networks, e.g., millions of nodes. The key idea is to first embed the road networks into a low dimensional space for capturing the distance relations between vertices, get an embedded vector for each vertex, and then perform a distance metric (L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> metric) on the embedded vectors to approximate shortest-path distances. We propose a hierarchical model to represent the embedding, and design an effective method to train the model. We also design a fine-tuning method to judiciously select high-quality training data. Extensive experiments on real-world datasets show that our embedding based approach significantly outperforms the state-of-the-art methods.