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Online Preference Weight Estimation Algorithm with Vanishing Regret for Car-Hailing in Road Network

Published: 24 August 2024 Publication History

Abstract

Car-hailing services play an important role in the modern transportation system, and the utilities of the service providers highly depend on the efficiency of route planning algorithms. A widely adopted route planning framework is to assign weights to roads and compute the routes with the shortest path algorithms. Existing techniques of weight-assigning often focus on the traveling time and length of the roads, but cannot incorporate with the preferences of the passengers (users).
In this paper, a set of preference weight estimation models is employed to capture the users' preferences over paths in car-hailing with their historical choices. Since the user preferences may vary dynamically over time, it is a challenging task to make real-time decisions over the models. The main technical contribution of this paper is to propose an online learning-based preference weight chasing (PWC) algorithm to solve this problem. The worst-case performance of PWC is analyzed with the metric regret, and it is proved that PWC has a vanishing regret, which means that the time-averaged loss concerning the fixed in-hindsight best model tends to zero. Experiments based on real-world datasets are conducted to verify the effectiveness and efficiency of our algorithm. The code is available at https://github.com/GaoYucen/PWC.

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cover image ACM Conferences
KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
August 2024
6901 pages
ISBN:9798400704901
DOI:10.1145/3637528
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Publication History

Published: 24 August 2024

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Author Tags

  1. dynamic deterministic markov decision process
  2. preference weight
  3. stateful online learning
  4. vanishing regret

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  • Research-article

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  • CCF-DiDi GAIA Collaborative Research Funds for Young Scholars
  • Science Fund Program of Shandong Province for Distinguished Oversea Young Scholars
  • National Natural Science Foundation of China
  • Shanghai Municipal Science and Technology Major Project

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KDD '24
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