Typical Snapshots Selection for Shortest Path Query in Dynamic Road Networks

TL;DR

This paper introduces a framework for selecting representative snapshots of dynamic road networks to improve shortest path query efficiency, using time-based and graph representation-based methods, validated by experiments.

Contribution

It proposes a novel snapshot selection framework that captures traffic dynamics and enables efficient path queries in real-time road networks.

Findings

Snapshot matching effectively finds similar traffic conditions.

The approach significantly speeds up shortest path queries.

Experiments validate the method's accuracy and efficiency.

Abstract

Finding the shortest paths in road network is an important query in our life nowadays, and various index structures are constructed to speed up the query answering. However, these indexes can hardly work in real-life scenario because the traffic condition changes dynamically, which makes the pathfinding slower than in the static environment. In order to speed up path query answering in the dynamic road network, we propose a framework to support these indexes. Firstly, we view the dynamic graph as a series of static snapshots. After that, we propose two kinds of methods to select the typical snapshots. The first kind is time-based and it only considers the temporal information. The second category is the graph representation-based, which considers more insights: edge-based that captures the road continuity, and vertex-based that reflects the region traffic fluctuation. Finally, we propose the snapshot matching to find the most similar typical snapshot for the current traffic condition and use its index to answer the query directly. Extensive experiments on real-life road network and traffic conditions validate the effectiveness of our approach.