Approximation algorithms for NP-hard problems
Shortest paths algorithms: theory and experimental evaluation
SODA '94 Proceedings of the fifth annual ACM-SIAM symposium on Discrete algorithms
Undirected single-source shortest paths with positive integer weights in linear time
Journal of the ACM (JACM)
Single-source shortest-paths on arbitrary directed graphs in linear average-case time
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
A Simple Shortest Path Algorithm with Linear Average Time
ESA '01 Proceedings of the 9th Annual European Symposium on Algorithms
Shortest Path Algorithms: An Evaluation Using Real Road Networks
Transportation Science
Proximity queries in large traffic networks
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
MARiO: multi-attribute routing in open street map
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
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When searching for optimal paths in a network, algorithms like A*-search need an approximation of the minimal costs between the current node and a target node. A reference node embedding is a universal method for making such an approximation working for any type of positive edge weights. A drawback of the approach is that the memory consumption of the embedding is linearly increasing with the number of attributes and landmarks. In this paper, we propose methods for significantly decreasing the memory consumption of embedded graphs and examine the impact of the landmark selection.