HTTE: A Hybrid Technique For Travel Time Estimation In Sparse Data Environments
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.
Cite
@article{arxiv.2301.05293,
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},
journal= {arXiv preprint arXiv:2301.05293},
year = {2023}
}
Comments
Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2019