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@COMMENT This file came from Kuldeep S. Meel's publication pages at
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@inproceedings{YLM23,
  title={Scalable Probabilistic Routes},
  author={Yang, Suwei and Liang, Victor and Meel, Kuldeep S.},
  abstract={
    Inference and prediction of routes have become of interest over the past
    decade owing to a dramatic increase in package delivery and ride-sharing
    services.
    Given the underlying combinatorial structure and the incorporation of
    probabilities,
    route prediction involves techniques from both formal methods and machine
    learning.
    One promising approach for predicting routes is using decision diagrams that
    are
    augmented with probability values. However, the effectiveness of this
    approach depends
    on the size of the compiled decision diagrams. The scalability of the
    approach is limited
    owing to its empirical runtime and space complexity. In this work, our
    contributions are
    two-fold: first, we introduce a relaxed encoding that uses a linear number
    of variables
    with respect to the number of vertices in a road network graph to
    significantly reduce the
    size of resultant decision diagrams. Secondly, instead of a stepwise
    sampling procedure,
    we propose a single pass sampling-based route prediction. In our evaluations
    arising from
    a real-world road network, we demonstrate that the resulting system achieves
    around twice
    the quality of suggested routes while being an order of magnitude faster
    compared to state-of-the-art.
  },
  year={2023},
  month=jun,
  booktitle=LPAR,
  bib2html_dl_pdf={../Papers/lpar23-ylm.pdf},
  bib2html_pubtype={Refereed Conference},
  bib2html_rescat={Sampling},
}