BusTr: Predicting Bus Travel Times from Real-Time Traffic
Abstract
We present BusTr, a machine-learned model for translating road traffic forecasts into predictions of bus delays, used by Google Maps to serve the majority of the world's public transit systems where no official real-time bus tracking is provided. We demonstrate that our neural sequence model improves over DeepTTE, the state-of-the-art baseline, both in performance (-30% MAPE) and training stability. We also demonstrate significant generalization gains over simpler models, evaluated on longitudinal data to cope with a constantly evolving world.
Keywords
Cite
@article{arxiv.2007.00882,
title = {BusTr: Predicting Bus Travel Times from Real-Time Traffic},
author = {Richard Barnes and Senaka Buthpitiya and James Cook and Alex Fabrikant and Andrew Tomkins and Fangzhou Xu},
journal= {arXiv preprint arXiv:2007.00882},
year = {2020}
}
Comments
14 pages, 2 figures, 5 tables. Citation: "Richard Barnes, Senaka Buthpitiya, James Cook, Alex Fabrikant, Andrew Tomkins, Fangzhou Xu (2020). BusTr: Predicting Bus Travel Times from Real-Time Traffic. 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. doi: 10.1145/3394486.3403376"