Combining Predicted and Live Traffic with Time-Dependent A* Potentials

TL;DR

This paper introduces a time-dependent A* algorithm that efficiently combines live and predicted traffic data for fast, exact shortest path routing on large road networks, enabling real-time navigation updates.

Contribution

It generalizes A* potentials to be time-dependent, allowing efficient integration of live and predicted traffic data for improved routing performance.

Findings

Achieves interactive query times on large networks.

Provides at least 100x speedup over Dijkstra's algorithm.

Outperforms previous time-independent A* potentials by up to 10x.

Abstract

We study efficient and exact shortest path algorithms for routing on road networks with realistic traffic data. For navigation applications, both current (i.e., live) traffic events and predictions of future traffic flows play an important role in routing. While preprocessing-based speedup techniques have been employed successfully to both settings individually, a combined model poses significant challenges. Supporting predicted traffic typically requires expensive preprocessing while live traffic requires fast updates for regular adjustments. We propose an A*-based solution to this problem. By generalizing A* potentials to time dependency, i.e. the estimate of the distance from a vertex to the target also depends on the time of day when the vertex is visited, we achieve significantly faster query times than previously possible. Our evaluation shows that our approach enables interactive query times on continental-sized road networks while allowing live traffic updates within a fraction of a minute. We achieve a speedup of at least two orders of magnitude over Dijkstra's algorithm and up to one order of magnitude over state-of-the-art time-independent A* potentials.