论文标题
BUSTR：预测实时流量的公共汽车旅行时间
BusTr: Predicting Bus Travel Times from Real-Time Traffic
论文作者
论文摘要
我们提出了Bustr，这是一种机器学习的模型，用于将道路交通预测转换为公交延迟的预测，Google Maps使用了为世界上大多数公共交通系统提供服务，在该系统中没有提供官方的实时巴士跟踪。我们证明，我们的神经序列模型在性能（MAPE）和训练稳定性方面都改进了DeepTTE，最先进的基线。我们还表现出对简单模型的显着概括，并在纵向数据上进行了评估，以应对不断发展的世界。
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.
