Route Planning in Transportation Networks

TL;DR

This paper surveys recent algorithmic advances in route planning for transportation networks, highlighting techniques for fast driving directions, public transit, and multimodal journeys, with trade-offs between preprocessing, space, and real-time traffic handling.

Contribution

It provides a comprehensive overview of state-of-the-art algorithms for route planning, emphasizing their performance, limitations, and applicability to different transportation modes and scales.

Findings

Driving directions can be computed in milliseconds at continental scale.

Exact public transit algorithms are fast for metropolitan systems but require simplifications for larger scales.

Multimodal route planning relies on approximate solutions due to complexity.

Abstract

We survey recent advances in algorithms for route planning in transportation networks. For road networks, we show that one can compute driving directions in milliseconds or less even at continental scale. A variety of techniques provide different trade-offs between preprocessing effort, space requirements, and query time. Some algorithms can answer queries in a fraction of a microsecond, while others can deal efficiently with real-time traffic. Journey planning on public transportation systems, although conceptually similar, is a significantly harder problem due to its inherent time-dependent and multicriteria nature. Although exact algorithms are fast enough for interactive queries on metropolitan transit systems, dealing with continent-sized instances requires simplifications or heavy preprocessing. The multimodal route planning problem, which seeks journeys combining schedule-based transportation (buses, trains) with unrestricted modes (walking, driving), is even harder, relying on approximate solutions even for metropolitan inputs.