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Customizable Routing with Learnings from Past Recommendations

Published: 22 November 2024 Publication History

Abstract

Finding routes in road networks is a fundamental task for routing services, but most existing methods only consider the network topology and properties and cannot handle semantic queries that express user preferences or constraints. We present a novel method that leverages historical route recommendations to prune irrelevant paths and speed up the search process. Moreover, we introduce a probabilistic modeling for path finding that can incorporate query semantics, such as "route from Seattle to Redmond with less traffic lights", and find optimal routes that satisfy them. We conduct experiments and evaluations on real-world datasets and show that our method outperforms the state-of-the-art methods in terms of runtime efficiency and route quality and can effectively answer semantic queries.

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cover image ACM Conferences
SIGSPATIAL '24: Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems
October 2024
743 pages
This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 November 2024

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Author Tags

  1. path finding
  2. routing
  3. semantic routing
  4. shortest path

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  • Short-paper
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SIGSPATIAL '24
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SIGSPATIAL '24 Paper Acceptance Rate 37 of 122 submissions, 30%;
Overall Acceptance Rate 257 of 1,238 submissions, 21%

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