• Corpus ID: 17849805

Computing Point-to-Point Shortest Paths from External Memory

@inproceedings{Goldberg2005ComputingPS,
  title={Computing Point-to-Point Shortest Paths from External Memory},
  author={Andrew V. Goldberg and Renato F. Werneck},
  booktitle={ALENEX/ANALCO},
  year={2005}
}
We study the ALT algorithm [19] for the point-to-point shortest path problem in the context of road networks. We suggest improvements to the algorithm itself and to its preprocessing stage. We also develop a memory-efficient implementation of the algorithm that runs on a Pocket PC. It stores graph data in a flash memory card and uses RAM to store information only for the part of the graph visited by the current shortest path computation. The implementation works even on very large graphs… 
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cs.princeton.edu
174 Citations
Highly Influential Citations
15
Background Citations
66
Methods Citations
75
Results Citations
2

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174 Citations

PHAST: Hardware-Accelerated Shortest Path Trees
Reach for A*: Efficient Point-to-Point Shortest Path Algorithms
TLDR
This work introduces a bidirectional version of the reach-based approach of Gutman [17] that uses implicit lower bounds and adds shortcut arcs to reduce vertex reaches and greatly improves both preprocessing and query times.
Reach for A*: Shortest Path Algorithms with Preprocessing
TLDR
This work combines a reach-based algorithm with landmark-based A search to obtain a wide range of space-time trade-offs and shows how the addition of shortcuts to the graph greatly improves the performance of both preprocessing and queries.
Landmark routing for large graphs in fixed-memory environments
TLDR
A new heuristic for the A* algorithm that references a data structure of size θ(|L|2 + |V|), where L represents a set of strategically chosen landmark vertices and V the set of vertices in the graph, is introduced.
Better Landmarks Within Reach
TLDR
A practical algorithm for the point-to-point shortest path problem on road networks that combines A* search, landmark-based lower bounds, and reach-based pruning is presented, which makes preprocessing and queries faster while reducing the overall space requirements.
Optimizing Landmark-Based Routing and Preprocessing
TLDR
This work significantly improves the classic ALT (A* + Landmarks + Triangle equality) algorithm by carefully optimizing both preprocessing and query phases, effectively minimize preprocessing time to a few seconds, making the ALT algorithm also suitable for dynamic scenarios, i.e., road networks with changing edge weights due to traffic updates.
Point-to-Point Shortest Path Algorithms with Preprocessing
TLDR
A survey of some recent results on point-to-point shortest path algorithms and recent heuristics that solve the problem while examining only a small portion of the input graph; the graph can be very big.
Re-optimizing the Performance of Shortest Path Queries Using Parallelized Combining Speedup Technique based on Bidirectional Arc flags and Multilevel Approach
TLDR
A new combined speedup technique based on three speedup techniques were combined and each technique is parallelised individually and the performance of the combination is measured with respect to pre- processing time, runtime and number of nodes visited in random graphs, planar graphs and real world data sets.
Optimizing shortest path queries with parallelized arc flags
  • R. Kalpana, P. Thambidurai
  • Computer Science
    2011 International Conference on Recent Trends in Information Technology (ICRTIT)
  • 2011
TLDR
The combined speedup techniques are utilized along with Dijkstra's algorithm to further improve the performance in terms of runtime and number of vertices visited in the graph.
Efficient Maintenance of All-Pairs Shortest Distances
TLDR
This paper addresses the problem of maintaining all-pairs shortest distances in dynamic graphs and proposes novel efficient incremental algorithms, working both in main memory and on disk, which significantly outperforms state-of-the-art algorithms.
...
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