Abstract
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 functions rather than fixed costs. In this time-dependent alternative route scenario, we present a novel query algorithm, called Time-Dependent Alternative Graph (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 collection phase constructs candidate alternative routes; consequently, a pruning phase 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.
Highlights
Querying a route planning service is a common daily-routine activity
We presented Time-Dependent Alternative Graph (TDAG), a novel algorithm for computing alternative routes in FIFO-abiding time-dependent road networks based on succinctly stored preprocessed travel information
One of TDAG’s strong features is that it can smoothly trade-off the quality of the resulting alternative graph with the required execution time via proper choices of its parameter N
Summary
Querying a route planning service is a common daily-routine activity. The majority of such services, as well as of the underlying route planning algorithms, answer queries by offering a route plan from an origin o to a destination d [1]. Each arc is accompanied with a positive scalar (a.k.a. weight) representing its usage cost (e.g., distance or traversal-time), with respect to the assumed cost-metric. There is a given optimization criterion (e.g., the total distance or earliest arrival time) for the selection of routes [1]. The most typical case for daily commuters assumes that the arc-costs metric consists of traversal-times for the arcs, and the optimization criterion is the earliest arrival time at the destination
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