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Adaptive Fastest Path Computation on a Road Network: A Traffic Mining Approach

Summary: Adaptive fastest-path search mines historical traffic to identify frequently traveled, context-effective routes beyond Euclidean distance and fixed speeds. Road-hierarchy partitioning, selective precomputation, and data-supported edge pruning yield short, well-supported paths with substantially lower query cost. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
9821
Venue
VLDB
Year
2007
Pagerank
7.0999891e-05
Overall Rank
3,817 | 73.82%
DOI
10.14778/1325851.1325944

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Authors

BibTeX Citation

@article{gonzalez_vldb07,
        title = {{Adaptive Fastest Path Computation on a Road Network: A Traffic Mining Approach}},
        author = {Gonzalez, Hector and Han, Jiawei and Li, Xiaolei and Myslinska, Margaret and Sondag, John Paul},
        journal = {PVLDB},
        series = {{VLDB} '07},
        volume = {30},
        number = {2},
        pages = {794--805},
        doi = {10.14778/1325851.1325944},
        url = {https://doi.org/10.14778/1325851.1325944},
        year = {2007}
}

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