BusTr: Predicting Bus Travel Times from Real-Time Traffic

• URL: http://arxiv.org/abs/2007.00882v1
• Date: Thu, 2 Jul 2020 05:05:23 GMT
• Title: BusTr: Predicting Bus Travel Times from Real-Time Traffic
• Authors: Richard Barnes and Senaka Buthpitiya and James Cook and Alex Fabrikant and Andrew Tomkins and Fangzhou Xu
• Abstract summary: We present BusTr, a machine-learned model for translating road traffic forecasts into predictions of bus delays. It is used by Google Maps to serve the majority of the world's public transit systems where no official real-time bus tracking is provided.
• Score: 11.832652376678295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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