  Considering deep sequence learning for practical application, two
representative RNNs - LSTM and GRU may come to mind first. Nevertheless, is
there no chance for other RNNs? Will there be a better RNN in the future? In
this work, we propose a novel, succinct and promising RNN - Fusion Recurrent
Neural Network (Fusion RNN). Fusion RNN is composed of Fusion module and
Transport module every time step. Fusion module realizes the multi-round fusion
of the input and hidden state vector. Transport module which mainly refers to
simple recurrent network calculate the hidden state and prepare to pass it to
the next time step. Furthermore, in order to evaluate Fusion RNN's sequence
feature extraction capability, we choose a representative data mining task for
sequence data, estimated time of arrival (ETA) and present a novel model based
on Fusion RNN. We contrast our method and other variants of RNN for ETA under
massive vehicle travel data from DiDi Chuxing. The results demonstrate that for
ETA, Fusion RNN is comparable to state-of-the-art LSTM and GRU which are more
complicated than Fusion RNN.
