Highway Hierarchies Hasten Exact Shortest Path Queries
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1 Sanders/Schultes: Highway Hierarchies 1 Highway Hierarchies Hasten Exact Shortest Path Queries Peter Sanders and Dominik Schultes Universität Karlsruhe October 2005
2 Sanders/Schultes: Highway Hierarchies 2 How do I get there from here? Applications route planning systems in the internet (e.g. car navigation systems
3 Sanders/Schultes: Highway Hierarchies 3 Goals exact shortest (i.e. fastest) paths in large road networks fast queries fast preprocessing low space consumption? scale-invariant, i.e., optimised not only for long paths
4 Sanders/Schultes: Highway Hierarchies 4 Related Work method query prepr. space scale source basic A [Hart et al. 68] bidirected [Pohl 71] heuristic hwy hier [commercial] separator hierarchies o? [Wagner et al. 02] geometric containers [Wagner et al. 03] bitvectors ++ o [Lauther... 04] reach based o o + + [Gutman 04] landmarks + ++ [Goldberg et al. 04] highway hierarchies here
5 Sanders/Schultes: Highway Hierarchies 5 DIJKSTRA s Algorithm not practicable Dijkstra s t for large road networks (e.g. Western Europe: nodes) bidirectional Dijkstra s t improves the running time, but still too slow
6 Sanders/Schultes: Highway Hierarchies 6 Commercial Systems 1. Search from the source and target node ( bidirectional ) within a certain radius (e.g. 20 km), consider all roads 2. Continue the search within a larger radius (e.g. 100 km), consider only national roads and motorways 3. Continue the search, consider only motorways fast, but not exact
7 Sanders/Schultes: Highway Hierarchies 7 Exact Highway Hierarchies complete search within a local area s t search in a (thinner) highway network = minimal graph that preserves all shortest paths contract trees and lines iterate highway hierarchy s t
8 Sanders/Schultes: Highway Hierarchies 8 A Meaning of Local Dijkstra s Algorithm from node s nodes are settled in a fixed order Dijkstra rank r s (v) of node v = rank w.r.t. this order For a parameter H, d H (s) := d(s,u), where r s (u) = H 9 8 r s = s 3 N 5 (s) H-neighbourhood of s: N H (s) := {v V d(s,v) d H (s)}
9 Sanders/Schultes: Highway Hierarchies 9 Highway Network N H (s) N H (t) s t Highway Edge (u,v) belongs to highway network iff there are nodes s and t s.t. (u,v) is on the shortest path from s to t and v N H (s) and u N H (t)
10 Sanders/Schultes: Highway Hierarchies 10 Construction Example: Western Europe, bounding box around Karlsruhe
11 Sanders/Schultes: Highway Hierarchies 11 Complete Road Network
12 Sanders/Schultes: Highway Hierarchies 12 Highway Network
13 Sanders/Schultes: Highway Hierarchies 13 Attached Treesgy
14 Sanders/Schultes: Highway Hierarchies 14 2-Coregy
15 Sanders/Schultes: Highway Hierarchies 15 Contract Linesgy
16 Sanders/Schultes: Highway Hierarchies 16 Highway Hierarchy: Level 1 and Level 2
17 Sanders/Schultes: Highway Hierarchies 17 Fast Construction Phase 1: Construction of Partial Shortest Path Trees For each node s 0, perform an SSSP search from s 0. A node s state is either active or passive. active s 0 is active. passive A node inherits the state of its parent in the shortest path tree. s 0 If the abort condition is fulfilled for a node p, p s state is set to passive. The search is aborted when all queued nodes are passive.
18 Sanders/Schultes: Highway Hierarchies 18 Fast Construction Abort Condition: N H (s 1 ) s 0 s 1 p N H (p) p is set to passive iff N H (s 1 ) N H (p) 1
19 Sanders/Schultes: Highway Hierarchies 19 Fast Construction, Phase 2 Theorem: The tree roots and leaves encountered in Phase 1 witness all highway edges. The highway edges can be found in time linear in the tree sizes. N H (s 0 ) N H (t 0 ) leaf of partial s 0 t 0 shortest path tree Highway
20 Sanders/Schultes: Highway Hierarchies 20 Query Bidirectional version of Dijkstra s Algorithm Restrictions: Do not leave the neighbourhood of the entrance point to the current level. Instead: switch to the next level. s N (v) level 1 v level 0 N (s) Do not enter a tree or a line. entrance point to level 0 entrance point to level 1 entrance point to level 2
21 Sanders/Schultes: Highway Hierarchies 21 Query Theorem: We still find the shortest path.
22 Sanders/Schultes: Highway Hierarchies 22 Query Example: from Karlsruhe, Am Fasanengarten 5 to Palma de Mallorca
23 Sanders/Schultes: Highway Hierarchies 23 Bounding Box: 20 km Level 0 Search Space
24 Sanders/Schultes: Highway Hierarchies 24 Bounding Box: 20 km Level 0 Search Space
25 Sanders/Schultes: Highway Hierarchies 25 Bounding Box: 20 km Level 1 Search Space
26 Sanders/Schultes: Highway Hierarchies 26 Bounding Box: 20 km Level 1 Search Space
27 Sanders/Schultes: Highway Hierarchies 27 Bounding Box: 20 km Level 2 Search Space
28 Sanders/Schultes: Highway Hierarchies 28 Bounding Box: 20 km Level 2 Search Space
29 Sanders/Schultes: Highway Hierarchies 29 Bounding Box: 20 km Level 3 Search Space
30 Sanders/Schultes: Highway Hierarchies 30 Bounding Box: 80 km Level 4 Search Space
31 Sanders/Schultes: Highway Hierarchies 31 Bounding Box: 400 km Level 6 Search Space
32 Sanders/Schultes: Highway Hierarchies 32 Level 8 Search Space
33 Sanders/Schultes: Highway Hierarchies 33 Level 10 Search Space
34 Sanders/Schultes: Highway Hierarchies 34 Experiments W. Europe (PTV) Our Inputs USA (Tiger Line) #nodes #edges #road categories speed range [km/h] :43 preproc time 1 [h] 4:20 1 using a faster method that computes supersets of the highway networks
35 Sanders/Schultes: Highway Hierarchies 35 Shrinking of the Highway Networks Europe H = 75 H = 125 H = 175 H = #edges level
36 Sanders/Schultes: Highway Hierarchies 36 Queries Time Query Time [ms] USA Europe Dijkstra Rank
37 Sanders/Schultes: Highway Hierarchies 37 Queries Speedup Speedup (#settled nodes) USA Europe Dijkstra Rank
38 Sanders/Schultes: Highway Hierarchies 38 Conclusion exact shortest (i.e. fastest) paths in large road networks highway network e.g. Europe nodes preserves shortest paths fast queries < 8 ms on average fast preprocessing 3 hours reasonable space consumption 1.8 GB, improvable scale-invariant, i.e., optimised not only for long paths multilevel approach
39 Sanders/Schultes: Highway Hierarchies 39 Future Work speed up preprocessing (e.g. parallelisation) speed up queries (e.g. combination with goal directed approaches like landmarks, geometric containers, or bit vectors) fast local updates of the highway network (e.g. due to traffic jams) external memory
40 Sanders/Schultes: Highway Hierarchies 40 Summary of the Results input params constr. query USA Europe Germany #nodes #edges #degree 2 nodes #road categories average speeds [km/h] H CPU time [h] #levels avg. CPU time [ms] #settled nodes speedup (CPU time) speedup (#settled nodes) efficiency 112% 34% 13% main memory usage [MB] (346)
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