Fusion Recurrent Neural Network
This work addresses the need for efficient and effective RNNs in data mining tasks like ETA, though it appears incremental as it matches rather than surpasses existing methods.
The authors tackled the problem of improving sequence learning for practical applications by proposing a novel Fusion Recurrent Neural Network (Fusion RNN), which achieved comparable performance to state-of-the-art LSTM and GRU on an estimated time of arrival task using massive vehicle travel data from DiDi Chuxing.
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.