Time-Dependent Alternative Route Planning: Theory and Practice

We consider the problem of computing a set of meaningful alternative origin-to-destination routes, in real-world road network instances whose arcs are accompanied by travel-time <i>functions</i> rather than fixed costs. In this time-dependent alternative route scenario, we present a nove...

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Bibliographic Details
Main Authors: Spyros Kontogiannis, Andreas Paraskevopoulos, Christos Zaroliagis
Format: Article
Language:English
Published: MDPI AG 2021-07-01
Series:Algorithms
Subjects:
Online Access:https://www.mdpi.com/1999-4893/14/8/220
Description
Summary:We consider the problem of computing a set of meaningful alternative origin-to-destination routes, in real-world road network instances whose arcs are accompanied by travel-time <i>functions</i> rather than fixed costs. In this time-dependent alternative route scenario, we present a novel query algorithm, called <i>Time-Dependent Alternative Graph</i> (TDAG), that exploits the outcome of a time-consuming preprocessing phase to create a manageable amount of travel-time metadata, in order to provide answers for arbitrary alternative-routes queries, in only a few milliseconds for continental-size instances. The resulting set of alternative routes is aggregated in the form of a time-dependent alternative graph, which is characterized by the minimum route overlap, small stretch factor, small size, and low complexity. To our knowledge, this is the first work that deals with the time-dependent setting in the framework of alternative routes. The preprocessed metadata prescribe the minimum travel-time informations between a small set of “landmark” nodes and all other nodes in the graph. The TDAG query algorithm carries out the work in two distinct phases: initially, a <i>collection phase</i> constructs candidate alternative routes; consequently, a <i>pruning phase</i> cautiously discards uninteresting or low-quality routes from the candidate set. Our experimental evaluation on real-world, time-dependent road networks demonstrates that TDAG performed much better (by one or two orders of magnitude) than the existing baseline approaches.
ISSN:1999-4893