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Hierarchical Cut Labelling – Scaling Up Distance Queries on Road Networks

Summary: Hierarchical cut 2-hop labelling (HC2L) merges hub/highway and tree-decomposition ideas via a balanced tree hierarchy to shrink distance labels and prune query search space. HC2Lp parallelizes preprocessing; on 10 real road networks it delivers 1.5–4x faster queries and up to 60% smaller labels, with comparable build times. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6808
Venue
SIGMOD
Year
2023
Pagerank
5.9251728e-05
Overall Rank
6,292 | 56.84%
DOI
10.1145/3626731

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{farhan_sigmod23,
        title = {{Hierarchical Cut Labelling – Scaling Up Distance Queries on Road Networks}},
        author = {Farhan, Muhammad and Koehler, Henning and Ohms, Robert and Wang, Qing},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3626731},
        url = {https://dl.acm.org/doi/10.1145/3626731},
        year = {2023}
}

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