A Fast and Tight Heuristic for A* in Road Networks

TL;DR

This paper introduces CH-Potentials, a fast heuristic for A* in road networks that leverages Contraction Hierarchies to provide tight distance estimates, improving route planning efficiency and flexibility.

Contribution

We develop CH-Potentials, a novel heuristic derived from Contraction Hierarchies, enabling efficient and flexible A* routing in large road networks.

Findings

CH-Potentials significantly speeds up A* searches.

The heuristic provides tight distance estimates for practical routing.

Optimizations for low degree nodes further improve performance.

Abstract

We study exact, efficient and practical algorithms for route planning in large road networks. Routing applications often require integrating the current traffic situation, planning ahead with traffic predictions for the future, respecting forbidden turns, and many other features depending on the exact application. While Dijkstra's algorithm can be used to solve these problems, it is too slow for many applications. A* is a classical approach to accelerate Dijkstra's algorithm. A* can support many extended scenarios without much additional implementation complexity. However, A*'s performance depends on the availability of a good heuristic that estimates distances. Computing tight distance estimates is a challenge on its own. On road networks, shortest paths can also be quickly computed using hierarchical speedup techniques. They achieve speed and exactness but sacrifice A*'s flexibility. Extending them to certain practical applications can be hard. In this paper, we present an algorithm to efficiently extract distance estimates for A* from Contraction Hierarchies (CH), a hierarchical technique. We call our heuristic CH-Potentials. Our approach allows decoupling the supported extensions from the hierarchical speed-up technique. Additionally, we describe A* optimizations to accelerate the processing of low degree nodes, which often occur in road networks.