Accepted Papers

BusTr: predicting bus travel times from real-time traffic

Richard Barnes: UC Berkeley; Senaka Buthpitiya: Google Research; James Cook: N ne; Alex Fabrikant: Google Research; Andrew Tomkins: Google Research; Fangzhou Xu: Google Research


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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