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BusTr: Predicting Bus Travel Times from Real-Time Traffic

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arxiv 2007.00882 v1 pith:5RE7TUHO submitted 2020-07-02 cs.LG stat.ML

BusTr: Predicting Bus Travel Times from Real-Time Traffic

Richard Barnes , Senaka Buthpitiya , James Cook , Alex Fabrikant , Andrew Tomkins , Fangzhou Xu This is my paper
classification cs.LG stat.ML
keywords bustrdemonstratemodelreal-timetrafficworldbaselineconstantly
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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