BusTr
Richard Barnes, Senaka Buthpitiya, James Cook, Alex Fabrikant, Andrew Tomkins, Fangzhou Xu
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.
Topics & Concepts
Computer scienceGeneralizationPublic transportArtificial intelligenceArtificial neural networkSequence (biology)Data modelingTracking (education)Tracking systemTraining setTrajectoryData miningData collectionTraining (meteorology)Machine learningMathematical modelTraffic Prediction and Management TechniquesTransportation Planning and OptimizationHuman Mobility and Location-Based Analysis