Algorithm Engineering for Realistic Journey Planning in Transportation Networks

Diese Dissertation beschäftigt sich mit der Routenplanung in Transportnetzen. Es werden neue, effiziente algorithmische Ansätze zur Berechnung optimaler Verbindungen in öffentlichen Verkehrsnetzen, Straßennetzen und multimodalen Netzen, die verschiedene Transportmodi miteinander verknüpfen, eingeführt. Im Fokus der Arbeit steht dabei die Praktikabilität der Ansätze, was durch eine ausführliche experimentelle Evaluation belegt wird

Dynamic Time-Dependent Route Planning in Road Networks with User Preferences

There has been tremendous progress in algorithmic methods for computing driving directions on road networks. Most of that work focuses on time-independent route planning, where it is assumed that the cost on each arc is constant per query. In practice, the current traffic situation significantly influences the travel time on large parts of the road network, and it changes over the day. One can distinguish between traffic congestion that can be predicted using historical traffic data, and congestion due to unpredictable events, e.g., accidents. In this work, we study the \emph{dynamic and time-dependent} route planning problem, which takes both prediction (based on historical data) and live traffic into account. To this end, we propose a practical algorithm that, while robust to user preferences, is able to integrate global changes of the time-dependent metric~(e.g., due to traffic updates or user restrictions) faster than previous approaches, while allowing subsequent queries that enable interactive applications

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

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

Towards Realistic Pedestrian Route Planning

Pedestrian routing has its specific set of challenges, which are often neglected by state-of-the-art route planners. For instance, the lack of detailed sidewalk data and the inability to traverse plazas and parks in a natural way often leads to unappealing and suboptimal routes. In this work, we first propose to augment the network by generating sidewalks based on the street geometry and adding edges for routing over plazas and squares. Using this and further information, our query algorithm seamlessly handles node-to-node queries and queries whose origin or destination is an arbitrary location on a plaza or inside a park. Our experiments show that we are able to compute appealing pedestrian routes at negligible overhead over standard routing algorithms

UniALT for regular language contrained shortest paths on a multi-modal transportation network

Shortest paths on road networks can be efficiently calculated using Dijkstra\u27s algorithm (D). In addition to roads, multi-modal transportation networks include public transportation, bicycle lanes, etc. For paths on this type of network, further constraints, e.g., preferences in using certain modes of transportation, may arise. The regular language constrained shortest path problem deals with this kind of problem. It uses a regular language to model the constraints. The problem can be solved efficiently by using a generalization of Dijkstra\u27s algorithm (D_RegLC). In this paper we propose an adaption of the speed-up technique uniALT, in order to accelerate D_RegLC. We call our algorithm SDALT. We provide experimental results on a realistic multi-modal public transportation network including time-dependent cost functions on arcs. The experiments show that our algorithm performs well, with speed-ups of a factor 2 to 20