  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.
