Core Routing on Dynamic Time-Dependent Road Networks

Route planning in large scale time-dependent road networks is an important practical application of the shortest paths problem that greatly benefits from speedup techniques. In this paper we extend a two-level hierarchical approach for pointto-point shortest paths computations to the time-dependent case. This method, also known as core routing in the literature for static graphs, consists in the selection of a small subnetwork where most of the computations can be carried out, thus reducing the search space. We combine this approach with bidirectional goal-directed search in order to obtain an algorithm capable of finding shortest paths in a matter of milliseconds on continental sized networks. Moreover, we tackle the dynamic scenario where the piecewise linear functions that we use to model time-dependent arc costs are not fixed, but can have their coefficients updated requiring only a small computational effort

Faster Transit Routing by Hyper Partitioning

We present a preprocessing-based acceleration technique for computing bi-criteria Pareto-optimal journeys in public transit networks, based on the well-known RAPTOR algorithm [Delling et al 2015]. Our key idea is to first partition a hypergraph into cells, in which vertices correspond to routes (e.g., bus lines) and hyperedges to stops, and to then mark routes sufficient for optimal travel across cells. The query can then be restricted to marked routes and those in the source and target cells. This results in a practical approach, suitable for networks that are too large to be efficiently handled by the basic RAPTOR algorithm

Engineering and Augmenting Route Planning Algorithms

In this work, we introduce the first efficient, provably correct, algorithms for route planning in time-dependent and multi-criteria scenarios. Therefore, we follow the concept of algorithm engineering by designing, analyzing, implementing, and evaluating speed-up techniques for Dijkstra\u27s algorithm. As a result, we are able to compute best connections in continental-sized time-dependent transportatios networks (both of roads and railways) in the matter of a few milliseconds

Time dependent sharc routing

During the last years, many speed-up techniques for Dijkstra 's algorithm have been developed. As a result, computing a shortest path in a staticroad network is a matter of microseconds. However, only few of those techniques work in time-dependentnetworks. Unfortunately, such networks appear frequentely in reality: Roads are predictably congestured by traffic jams, and efficient timetable information systems rely on time-dependent networks. Hence, a fast technique for routing in such networks is needed. In this work, we present an exacttime-dependent speed-up technique based on our recent SHARC-algorithm. As a result, we are able to efficiently compute shortest paths in time-dependent continental-sized transportation networks, both of roads and of railways. Document type: Part of book or chapter of boo

Arc-Flags in Dynamic Graphs

Computation of quickest paths has undergoing a rapid development in recent years. It turns out that many high-performance route planning algorithms are made up of several basic ingredients. However, not all of those ingredients have been analyzed in a emph{dynamic} scenario where edge weights change after preprocessing. In this work, we present how one of those ingredients, i.e., Arc-Flags can be applied in dynamic scenario

Faster Batched Shortest Paths in Road Networks

We study the problem of computing batched shortest paths in road networks efficiently. Our focus is on computing paths from a single source to multiple targets (one-to-many queries). We perform a comprehensive experimental comparison of several approaches, including new ones. We conclude that a new extension of PHAST (a recent one-to-all algorithm), called RPHAST, has the best performance in most cases, often by orders of magnitude. When used to compute distance tables (many-to-many queries), RPHAST often outperforms all previous approaches

Efficient Route Planning in Flight Networks

We present a set of three new time-dependent models with increasing flexibility for realistic route planning in flight networks. By these means, we obtain small graph sizes while modeling airport procedures in a realistic way. With these graphs, we are able to efficiently compute a set of best connections with multiple criteria over a full day. It even turns out that due to the very limited graph sizes it is feasible to precompute full distance tables between all airports. As a result, best connections can be retrieved in a few microseconds on real world data

Fast and Stable Repartitioning of Road Networks

We study the problem of graph partitioning for evolving road networks. While the road network of the world is mostly stable, small updates happen on a relatively frequent basis, as can been observed with the OpenStreetMap project (http://www.openstreetmap.org). For various reasons, professional applications demand the graph partition to stay roughly the same over time, and that changes are limited to areas where graph updates occur. In this work, we define the problem, present algorithms to satisfy the stability needs, and evaluate our techniques on continental-sized road networks. Besides the stability gains, we show that, when the changes are low and local, running our novel techniques is an order of magnitude faster than running graph partitioning from scratch