Multi-constraint shortest path using forest hop labeling

Abstract

The Multi-Constraint Shortest Path (MCSP) problem aims to find the shortest path between two nodes in a network subject to a given constraint set. It is typically processed as a skyline path problem. However, the number of intermediate skyline paths becomes larger as the network size increases and the constraint number grows, which brings about the dramatical growth of computational cost and further makes the existing index-based methods hardly capable of obtaining the complete exact results. In this paper, we propose a novel high-dimensional skyline path concatenation method to avoid the expensive skyline path search, which supports the efficient construction of hop labeling index for MCSP queries. Specifically, a set of insightful observations and techniques are proposed to improve the efficiency of concatenating two skyline path set, a n-Cube technique is designed to prune the concatenation space among multiple hops, and a constraint pruning method is used to avoid the unnecessary computation. Furthermore, to scale up to larger networks, we propose a novel forest hop labeling which enables the parallel label construction from different network partitions. Our approach is the first method that can achieve accuracy and efficiency for MCSP query. Extensive experiments on real-life road networks demonstrate the superiority of our method over state-of-the-art solutions.