HTTE: A Hybrid Technique For Travel Time Estimation In Sparse Data Environments

Nikolaos Zygouras, Nikolaos Panagiotou, Yang Li, Dimitrios Gunopulos, Leonidas Guibas
1/12/2023
cs.LG

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Abstract

Travel time estimation is a critical task, useful to many urban applications at the individual citizen and the stakeholder level. This paper presents a novel hybrid algorithm for travel time estimation that leverages historical and sparse real-time trajectory data. Given a path and a departure time we estimate the travel time taking into account the historical information, the real-time trajectory data and the correlations among different road segments. We detect similar road segments using historical trajectories, and use a latent representation to model the similarities. Our experimental evaluation demonstrates the effectiveness of our approach.

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Cite this paper

@article{zygouras2023htte,
  title  = {HTTE: A Hybrid Technique For Travel Time Estimation In Sparse Data Environments},
  author = {Nikolaos Zygouras and Nikolaos Panagiotou and Yang Li and Dimitrios Gunopulos and Leonidas Guibas},
  year   = {2023},
  eprint = {2301.05293},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2301.05293v1}
}

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