Incremental Updates of Contraction Hierarchies for Road Network Routing
نویسندگان
چکیده
منابع مشابه
Time Dependent Contraction Hierarchies -- Basic Algorithmic Ideas
Contraction hierarchies are a simple hierarchical routing technique that has proved extremely efficient for static road networks. We explain how to generalize them to networks with time-dependent edge weights. This is the first hierarchical speedup technique for time-dependent routing that allows bidirectional query algorithms.
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Time-Dependent Contraction Hierarchies is a routing technique that solves the shortest path problem in graphs with time-dependent edge weights, that have to satisfy the FIFO property. Although it shows great speedups over Dijkstra’s Algorithm the preprocessing is slow. We present a parallelized version of the preprocessing taking advantage of the multiple cores present in todays CPUs. Nodes ind...
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Contraction hierarchies are a simple hierarchical routing technique that has proved extremely efficient for static road networks. We explain how to generalize them to networks with time-dependent edge weights. This is the first hierarchical speedup technique for timedependent routing that allows bidirectional query algorithms. For large realistic networks with considerable time-dependence (Germ...
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Many speed-up techniques developed for accelerating the computation of shortest paths in road networks, like reach or contraction hierarchies, are based on the property that some streets are ’more important’ than others, e.g. on long routes the usage of an interstate is almost inevitable. In grids there is no obvious hierarchy among the edges, especially if the costs are uniform. Nevertheless w...
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Time-dependent Contraction Hierarchies provide fast and exact route planning for time-dependent large scale road networks but need lots of space. We solve this problem by the careful use of approximations of piecewise linear functions. This way we need about an order of magnitude less space while preserving exactness and accepting only a little slow down. Moreover, we use these approximations t...
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