Nowadays, it is possible to collect precise data describing movements of
public transport. Specifically, for each bus (or tram) geoposition data can be
regularly collected. This includes data for all buses in Warsaw, Poland.
Moreover, this data can be downloaded and analyzed. In this context, one of the
simplest questions is: can a model be build to represent behavior of busses,
and predict their delays. This work provides initial results of our attempt to
answer this question.

本研究使用波兰华沙的公交车定位数据，探讨了建立公交车行为模型及预测公交车延误的问题，并给出了初步的研究结果。

应用机器学习预测波兰华沙市公交运输行为

Applying machine learning to predict behavior of bus transport in Warsaw, Poland

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.

本文介绍了 BusTr，一个基于机器学习的模型，将交通预测转化为公交车延误预测，现已被 Google 地图用于为大部分世界公共交通系统提供服务，在没有官方实时公交跟踪的情况下。我们演示了我们的神经序列模型提高了训练稳定性和性能（MAPE 低 30%），并证明了在纵向数据上评估的更简单模型的显著泛化增益以应对不断发展的世界。