论文标题
FMA-ETA：完全基于FFN估算旅行时间
FMA-ETA: Estimating Travel Time Entirely Based on FFN With Attention
论文作者
论文摘要
估计到达时间（ETA）是智能运输系统中最重要的服务之一，近年来成为具有挑战性的时空（ST）数据挖掘任务。如今，基于深度学习的方法，特别是基于复发的神经网络（RNN）的方法，可以改编自ETA大量数据的ST模式并成为最先进的模式。但是，RNN正处于缓慢的训练和推理速度上，因为其结构对并行计算不友好。为了解决这个问题，我们提出了一个新颖，简短有效的框架，主要基于ETA的馈送网络（FFN），具有多因素自我注意力（FMA-ETA）的FFN。提出了新型的多因素自我发场机制来处理不同的类别特征并有目的地汇总信息。关于现实世界的车辆旅行数据集的广泛实验结果表明，就预测准确性而言，FMA-ETA与最先进的方法具有竞争力，并且推理速度明显更高。
Estimated time of arrival (ETA) is one of the most important services in intelligent transportation systems and becomes a challenging spatial-temporal (ST) data mining task in recent years. Nowadays, deep learning based methods, specifically recurrent neural networks (RNN) based ones are adapted to model the ST patterns from massive data for ETA and become the state-of-the-art. However, RNN is suffering from slow training and inference speed, as its structure is unfriendly to parallel computing. To solve this problem, we propose a novel, brief and effective framework mainly based on feed-forward network (FFN) for ETA, FFN with Multi-factor self-Attention (FMA-ETA). The novel Multi-factor self-attention mechanism is proposed to deal with different category features and aggregate the information purposefully. Extensive experimental results on the real-world vehicle travel dataset show FMA-ETA is competitive with state-of-the-art methods in terms of the prediction accuracy with significantly better inference speed.
