SPLZ: An Efficient Algorithm for Single Source Shortest Path Problem Using Compression Method

TL;DR

This paper presents SPLZ, an algorithm that compresses all-pairs shortest path data to efficiently solve the single source shortest path problem on large road networks, significantly reducing memory usage and computation time.

Contribution

The paper introduces a novel compression-based approach for precomputing and storing APSP, enabling fast SSSP queries with much lower memory requirements.

Findings

Achieves three orders of magnitude faster SSSP computation than Dijkstra with binary heap.

Compresses APSP data from 1TB to a few GB on large road networks.

Demonstrates effectiveness on a dataset with 1.2 million vertices.

Abstract

Efficient solution of the single source shortest path (SSSP) problem on road networks is an important requirement for numerous real-world applications. This paper introduces an algorithm for the SSSP problem using compression method. Owning to precomputing and storing all-pairs shortest path (APSP), the process of solving SSSP problem is a simple lookup of a little data from precomputed APSP and decompression. APSP without compression needs at least 1TB memory for a road network with one million vertices. Our algorithm can compress such an APSP into several GB, and ensure a good performance of decompression. In our experiment on a dataset about Northwest USA (with 1.2 millions vertices), our method can achieve about three orders of magnitude faster than Dijkstra algorithm based on binary heap.