13. [BDGMPSWW15] Route Planning in Transportation Networks. http://arxiv.org/abs/1504.05140v1
13
0.1 1 10 100 1,000 10,000
0.0001
0.001
0.01
0.1
1
10
100
1,000
Arc Flags
HH
HH*
SHARC
TNR with Arc Flags
HPML
CH
CCH
(customization)
TNR
HLC
CRP
Table Lookup
(PHAST)
Dijkstra’s Algorithm
Bidirectional Search
(customization)
(customization)
ALT
(customization)
CHASE
Hub Labels
Reach
REAL ReachFlags
HNR
CALT
Preprocessing time [min]
Querytime[ms]
Figure 7. Preprocessing and average query time performance for algorithms with available
experimental data on the road network of Western Europe, using travel times as edge weights.
Connecting lines indicate di erent trade-o s for the same algorithm. The figure is inspired by [238].
14. [BDGMPSWW15] Route Planning in Transportation Networks. http://arxiv.org/abs/1504.05140v1
14
Table 1. Performance of various speedup techniques on Western Europe. Column source indicates
the implementation tested for this survey.
data structures queries
impl. space time scanned time
algorithm source [GiB] [h:m] vertices [µs]
Dijkstra [75] 0.4 – 9 326 696 2 195 080
Bidir. Dijkstra [75] 0.4 – 4 914 804 1 205 660
CRP [77] 0.9 1:00 2 766 1 650
Arc Flags [75] 0.6 0:20 2 646 408
CH [77] 0.4 0:05 280 110
CHASE [75] 0.6 0:30 28 5.76
HLC [82] 1.8 0:50 – 2.55
TNR [15] 2.5 0:22 – 2.09
TNR+AF [40] 5.4 1:24 – 0.70
HL [82] 18.8 0:37 – 0.56
HL-Œ [5] 17.7 60:00 – 0.25
table lookup [75] 1 208 358.7 145:30 – 0.06
with existing approaches.
29. Pruned Highway Labeling
29
Table 1: Comparison of the performance between pruned highway labeling and previous methods. HL is
parallelized to use 12 cores in preprocessing and all other methods are not parallelized.
USA Europe
Preprocessing Space Query Preprocessing Space Query
Method [h:m] [GB] [ns] [h:m] [GB] [ns]
CH [5] 0:27 0.5 130000 0:25 0.4 180000
TNR [5] 1:30 5.4 3000 1:52 3.7 3400
TNR+AF [5] 2:37 6.3 1700 3:49 5.7 1900
HL local [1] 2:24 22.7 627 2:39 20.1 572
HL global [1] 2:35 25.4 266 2:45 21.3 276
HL-15 local [2] - - - 0:05 18.8 556
HL-∞ global [2] - - - 6:12 17.7 254
HLC-15 [7] 0:53 2.9 2486 0:50 1.8 2554
PHL-1 0:29 16.4 941 0:34 14.9 1039
5.2 Contraction Technique In this subsection, we
introduce a new technique called the contraction tech-
nique. First, we consider a vertex v of degree one.
Any shortest path from v to another vertex passes
through its adjacent vertex w. Moreover, the vertex v
is never contained in shortest paths between other ver-
usage. Moreover, this also makes the query time faster
because we can reduce unnecessary comparisons when
the indexes do not match in two triples. For more ef-
ficient implementation, we use pointer arithmetic and
align arrays storing labels to cache line.
[AIKK14] Fast Shortest-path Distance Queries on Road Networks by Pruned Highway Labeling.
http://epubs.siam.org/doi/abs/10.1137/1.9781611973198.14
37. References
• [Goldberg08] Goldberg, A.V.: A Practical Shortest Path Algorithm with Linear Expected
Time. SIAM Journal on Computing 37, 1637–1655 (2008)
• [AFGW10] I. Abraham, A. Fiat, A. V. Goldberg, and R. F. Werneck. Highway Dimension,
Shortest Paths, and Provably Efficient Algorithms. In Proceedings of the 21st Annual
ACM–SIAM Symposium on Discrete Algorithms (SODA’10), pages 782–793, 2010.
• [DGPW11] Daniel Delling , Andrew V. Goldberg , Thomas Pajor , Renato F. Werneck,
Customizable route planning, Proceedings of the 10th international conference on
Experimental algorithms, May 05-07, 2011, Crete, Greece.
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international conference on Experimental algorithms, May 05-07, 2011, Crete, Greece.
• [AIKK14] Takuya Akiba, Yoichi Iwata, Ken-ichi Kawarabayashi, and Yuki Kawata. Fast
Shortest-path Distance Queries on Road Networks by Pruned Highway Labeling. 2014
Proceedings of the Sixteenth Workshop on Algorithm Engineering and Experiments
(ALENEX). 2014, 147-154
37
38. References
• [BDGMPSWW15] Hannah Bast, Daniel Delling, Andrew Goldberg, Matthias Müller-
Hannemann, Thomas Pajor, Peter Sanders, Dorothea Wagner, and Renato Werneck.
Route Planning in Transportation Networks. arXiv:1504.05140. April 17, 2015.
• [AIY13] Takuya Akiba , Yoichi Iwata , Yuichi Yoshida, Fast exact shortest-path distance
queries on large networks by pruned landmark labeling, Proceedings of the 2013 ACM
SIGMOD International Conference on Management of Data, June 22-27, 2013, New York,
New York, USA.
• [Yoshida14] Yuichi Yoshida, Almost linear-time algorithms for adaptive betweenness
centrality using hypergraph sketches, Proceedings of the 20th ACM SIGKDD international
conference on Knowledge discovery and data mining, August 24-27, 2014, New York,
New York, USA.
38