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Dijkstra's Algorithm OnLine: An Empirical Case Study from Public Railroad Transport
 JOURNAL OF EXPERIMENTAL ALGORITHMICS
, 2000
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Geometric SpeedUp Techniques for Finding Shortest Paths in Large Sparse Graphs
, 2003
"... In this paper, we consider Dijkstra's algorithm for the single source single target shortest paths problem in large sparse graphs. The goal is to reduce the response time for online queries by using precomputed information. For the result of the preprocessing, we admit at most linear space. ..."
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Cited by 51 (14 self)
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In this paper, we consider Dijkstra's algorithm for the single source single target shortest paths problem in large sparse graphs. The goal is to reduce the response time for online queries by using precomputed information. For the result of the preprocessing, we admit at most linear space. We assume that a layout of the graph is given. From this layout, in the preprocessing, we determine for each edge a geometric object containing all nodes that can be reached on a shortest path starting with that edge. Based on these geometric objects, the search space for online computation can be reduced significantly. We present an extensive experimental study comparing the impact of different types of objects. The test data we use are traffic networks, the typical field of application for this scenario.
Using Multilevel Graphs for Timetable Information in Railway Systems
 IN PROCEEDINGS 4TH WORKSHOP ON ALGORITHM ENGINEERING AND EXPERIMENTS (ALENEX 2002), VOLUME 2409 OF SPRINGER LNCS
, 2002
"... In many fields of application shortest path finding problems in very large graphs arise. Scenarios where large numbers ofonW##O queries for shortest paths have to be processedin realtime appear for examplein tra#cinc5###HF5 systems.In such systems, the techn5Ww# con sidered to speed up the shortes ..."
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Cited by 28 (11 self)
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In many fields of application shortest path finding problems in very large graphs arise. Scenarios where large numbers ofonW##O queries for shortest paths have to be processedin realtime appear for examplein tra#cinc5###HF5 systems.In such systems, the techn5Ww# con sidered to speed up the shortest pathcomputation are usually basedon precomputed incomputed5 On approach proposedoften in thiscon text is a spacereduction where precomputed shortest paths are replaced by sin## edges with weight equal to thelenOq of the corresponres shortest path.In this paper, we give a first systematic experimen tal study of such a spacereduction approach. Wein troduce theconOkW of multilevel graph decomposition Foron specificapplication scenica from the field of timetable information in public tranc ort, we perform a detailed anai ysisan experimen tal evaluation of shortest path computation based on multilevel graph decomposition.
Combining SpeedUp Techniques for ShortestPath Computations
 In Proc. 3rd Workshop on Experimental and Efficient Algorithms. LNCS
, 2004
"... Computing a shortest path from one node to another in a directed graph is a very common task in practice. This problem is classically solved by Dijkstra's algorithm. Many techniques are known to speed up this algorithm heuristically, while optimality of the solution can still be guaranteed. ..."
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Cited by 23 (6 self)
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Computing a shortest path from one node to another in a directed graph is a very common task in practice. This problem is classically solved by Dijkstra's algorithm. Many techniques are known to speed up this algorithm heuristically, while optimality of the solution can still be guaranteed. In most studies, such techniques are considered individually.
Dynamic Shortest Paths Containers
, 2003
"... Using a set of geometric containers to speed up shortest path queries in a weighted graph has been proven a useful tool for dealing with large sparse graphs. Given a layout of a graph G = (V, E), we store, for each edge (u, v) E, the bounding box of all nodes t V for which a shortest utpath ..."
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Cited by 8 (3 self)
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Using a set of geometric containers to speed up shortest path queries in a weighted graph has been proven a useful tool for dealing with large sparse graphs. Given a layout of a graph G = (V, E), we store, for each edge (u, v) E, the bounding box of all nodes t V for which a shortest utpath starts with (u, v). Shortest path queries can then be answered by Dijkstra's algorithm restricted to edges where the corresponding bounding box contains the target. In this
Generating node coordinates for shortestpath computations in transportation networks
 ACM Journal on Experimental Algorithmics
"... Speedup techniques that exploit given node coordinates have proven useful for shortestpath computations in transportation networks and geographic information systems. To facilitate the use of such techniques when coordinates are missing from some, or even all, of the nodes in a network we generate ..."
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Cited by 2 (0 self)
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Speedup techniques that exploit given node coordinates have proven useful for shortestpath computations in transportation networks and geographic information systems. To facilitate the use of such techniques when coordinates are missing from some, or even all, of the nodes in a network we generate artificial coordinates using methods from graph drawing. Experiments on a large set of German train timetables indicate that the speedup achieved with coordinates from our drawings is close to that achieved with the true coordinates—and in some special cases even better.
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 Proceedings of TACAS’98
, 1998
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Geometric Shortest Path Containers
, 2004
"... In this paper, we consider Dijkstra's algorithm for the single source single target shortest path problem in large sparse graphs. The goal is to reduce the response time for online queries by using precomputed information. Due to the size of the graph, preprocessing space requirements can b ..."
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Cited by 2 (1 self)
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In this paper, we consider Dijkstra's algorithm for the single source single target shortest path problem in large sparse graphs. The goal is to reduce the response time for online queries by using precomputed information. Due to the size of the graph, preprocessing space requirements can be only linear in the number of nodes. We assume that a layout of the graph is given. In the preprocessing, we determine from this layout a geometric object for each edge containing all nodes that can be reached by a shortest path starting with that edge.
Service Supplier Relations for the Outsourcing of Information Processing Services
"... Today a large variety of services is available to everyone who has appropriate access to the internet. Open Service Markets (OSM) with a multitude of service types, service providers and service customers have come into being. Because of the large number of participating entities it can be assumed t ..."
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Cited by 1 (0 self)
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Today a large variety of services is available to everyone who has appropriate access to the internet. Open Service Markets (OSM) with a multitude of service types, service providers and service customers have come into being. Because of the large number of participating entities it can be assumed that certain services are offered by different service providers at the same time, but differing in their service quality. A core issue for OSMs is the question how service providers and service customers come together and how the interaction between them takes place. Most approaches are based on the trading concept and are dedicated to a single service use only. Based on an analysis of the OSM model we sketch the Service Supplier Relation (SSR) model supporting the repetitive use of services on a longterm basis. We have drawn inspiration from subcontracting industry which is widely accepted e.g. in the automotive industry. After discussing the SSR model we briefly describe the concept of Vi...
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"... Algorithms for timetable information systems are usually based on Dijkstra’s algorithm. The concrete scenario that we have in mind is a central information server in the realm of public railroad traffic on widearea networks. Due to the large size of the underlying timetables the efficiency of a nai ..."
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Algorithms for timetable information systems are usually based on Dijkstra’s algorithm. The concrete scenario that we have in mind is a central information server in the realm of public railroad traffic on widearea networks. Due to the large size of the underlying timetables the efficiency of a naive implementation is not acceptable in practice, so usually heuristics are used to improve the efficiency. Typically, using such heuristics means that the optimality of the solutions can no longer be guaranteed. In contrast, we investigate optimalitypreserving speedup techniques for Dijkstra’s algorithm. The basic question is whether algorithms that compute optimal solutions are competitive on contemporary computer technology. Therefore, we present the results of a computational study based on realworld data: the timetable that contains all German trains, and a