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Distributed TimeDependent Contraction Hierarchies
 In Proceedings of the 9th International Symposium on Experimental Algorithms, volume 6049 of LNCS
, 2010
"... Abstract. Server based route planning in road networks is now powerful enough to find quickest paths in a matter of milliseconds, even if detailed information on timedependent travel times is taken into account. However this requires huge amounts of memory on each query server and hours of preproce ..."
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Abstract. Server based route planning in road networks is now powerful enough to find quickest paths in a matter of milliseconds, even if detailed information on timedependent travel times is taken into account. However this requires huge amounts of memory on each query server and hours of preprocessing even for a medium sized country like Germany. This is a problem since global internet companies would like to work with transcontinental networks, detailed models of intersections, and regular repreprocessing that takes the current traffic situation into account. By giving a distributed memory parallelization of the arguably best current technique – timedependent contraction hierarchies, we remove these bottlenecks. For example, on a medium size network 64 processes accelerate preprocessing by a factor of 28 to 160 seconds, reduce per process memory consumption by a factor of 10.5 and increase query throughput by a factor of 25. Key words: timedependent shortest paths, distributed computation, message passing, algorithm engineering 1
Route Planning with Flexible Objective Functions
 In Proceedings of the 12th Workshop on Algorithm Engineering and Experiments (ALENEX’10), 124–137. SIAM
"... Abstract We present the first fast route planning algorithm that answers shortest paths queries for a customizable linear combination of two different metrics, e. g. travel time and energy cost, on large scale road networks. The precomputation receives as input a directed graph, two edge weight fun ..."
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Abstract We present the first fast route planning algorithm that answers shortest paths queries for a customizable linear combination of two different metrics, e. g. travel time and energy cost, on large scale road networks. The precomputation receives as input a directed graph, two edge weight functions t(e) and c(e), and a discrete interval [L, U ]. The resulting flexible query algorithm finds for a parameter p ∈ [L, U ] an exact shortest path for the edge weight t(e)+p·c(e). This allows for different tradeoffs between the two edge weight functions at query time. We apply precomputation based on node contraction, which adds all necessary shortcuts for any parameter choice efficiently. To improve the node ordering, we developed the new concept of gradual parameter interval splitting. Additionally, we improve performance by combining node contraction and a goaldirected technique in our flexible scenario.
Transit Node Routing Reconsidered?
"... Abstract. Transit Node Routing (TNR) is a fast and exact distance oracle for road networks. We show several new results for TNR. First, we give a surprisingly simple implementation fully based on Contraction Hierarchies that speeds up preprocessing by an order of magnitude approaching the time for j ..."
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Cited by 9 (2 self)
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Abstract. Transit Node Routing (TNR) is a fast and exact distance oracle for road networks. We show several new results for TNR. First, we give a surprisingly simple implementation fully based on Contraction Hierarchies that speeds up preprocessing by an order of magnitude approaching the time for just finding a Contraction Hierarchies (which alone has two orders of magnitude larger query time). We also develop a very effective purely graph theoretical locality filter without any compromise in query times. Finally, we show that a specialization to the online manytoone (or onetomany) shortest path further speeds up query time by an order of magnitude. This variant even has better query time than the fastest known previous methods which need much more space. 1 Introduction and Related Work Route planning in road networks has seen a lot of results from the algorithm engineering community in recent years. With Dijkstra’s seminal algorithm being the baseline, a number of techniques preprocess the static input graph to achieve drastic speedups. Contraction Hierarchies (CH) [1,2] is a speeduptechnique that has a convenient tradeoff between preprocessing effort and query efficiency. Road network with millions of nodes
Efficient routing in road networks with turn costs
 In 10th Int. Symposium on Experimental Algorithms (SEA
, 2011
"... Abstract. We present an efficient algorithm for shortest path computation in road networks with turn costs. Each junction is modeled as a node, and each road segment as an edge in a weighted graph. Turn costs are stored in tables that are assigned to nodes. By reusing turn cost tables for identical ..."
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Cited by 9 (0 self)
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Abstract. We present an efficient algorithm for shortest path computation in road networks with turn costs. Each junction is modeled as a node, and each road segment as an edge in a weighted graph. Turn costs are stored in tables that are assigned to nodes. By reusing turn cost tables for identical junctions, we improve the space efficiency. Preprocessing based on an augmented node contraction allows fast shortest path queries. Compared to an edgebased graph, we reduce preprocessing time by a factor of 3.4 and space by a factor of 2.4 without change in query time. Key words: route planning; banned turn; turn cost; algorithm engineering 1
Contraction of Timetable Networks with Realistic Transfers
, 2009
"... We successfully contract timetable networks with realistic transfer times. Contraction gradually removes nodes from the graph and adds shortcuts to preserve shortest paths. This reduces query times to 1ms with preprocessing times around 6 minutes on all tested instances. We achieve this by an improv ..."
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Cited by 3 (0 self)
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We successfully contract timetable networks with realistic transfer times. Contraction gradually removes nodes from the graph and adds shortcuts to preserve shortest paths. This reduces query times to 1ms with preprocessing times around 6 minutes on all tested instances. We achieve this by an improved contraction algorithm and by using a station graph model. Every node in our graph has a onetoone correspondence to a station and every edge has an assigned collection of connections. Our graph model does not need parallel edges. The query algorithm does not compute a single earliest arrival time at a station but a set of arriving connections that allow best transfer opportunities. 1