论文标题
部分可观测时空混沌系统的无模型预测
DeeprETA: An ETA Post-processing System at Scale
论文作者
论文摘要
估计到达时间（ETA）在交付和乘车平台中起着重要作用。例如，Uber使用ETA来计算票价，估算接送时间，将骑手与驾驶员进行匹配，计划交付等等。常用的路线规划算法预测了在最佳可用路线上条件的ETA，但是当未知的实际途径不知道时，此类ETA估计可能是不可靠的。在本文中，我们描述了一个ETA后处理系统，其中深层残留ETA网络（DeepReTa）完善了由路线计划算法产生的天真ETA。离线实验和在线测试表明，DeepReTa的后加工可以显着提高Naive ETA的准确性，如平均值和中值绝对误差所衡量。我们进一步表明，DeepReTa的后处理比竞争性基线回归模型较低。
Estimated Time of Arrival (ETA) plays an important role in delivery and ride-hailing platforms. For example, Uber uses ETAs to calculate fares, estimate pickup times, match riders to drivers, plan deliveries, and more. Commonly used route planning algorithms predict an ETA conditioned on the best available route, but such ETA estimates can be unreliable when the actual route taken is not known in advance. In this paper, we describe an ETA post-processing system in which a deep residual ETA network (DeeprETA) refines naive ETAs produced by a route planning algorithm. Offline experiments and online tests demonstrate that post-processing by DeeprETA significantly improves upon the accuracy of naive ETAs as measured by mean and median absolute error. We further show that post-processing by DeeprETA attains lower error than competitive baseline regression models.
