Research Article

Multistep Prediction of Bus Arrival Time with the Recurrent Neural Network

Table 3

Seven RNN network model structures and their parameters.

ClassificationType of networkType of layerOutput sequenceNumber of parametersTotal number of parameters

Pure RNNPure GRUGRU(None, 41, 64)1286415,009
Dense(None, 32)1056
Dense(None, 33)1089
Pure LSTMLSTM(None, 64)1689620,065
Dense(None, 32)2080
Dense(None, 33)1089

Multi-input hybrid modelGRU-BPInput layer[(None, 32, 1)]025,089
Input layer[(None, 9)]0
GRU(None, 32, 64)12864
Dense(None, 16)160
GRU(None, 32)9408
Concatenate(None, 48)0
Dense(None, 32)1568
Dense(None, 33)1089
LSTM-BPInput layer[(None, 32, 1)]032,129
Input layer[(None, 9)]0
LSTM(None, 32, 64)16896
Dense(None, 16)160
LSTM(None, 32)12416
Concatenate(None, 48)0
Dense(None, 32)1568
Dense(None, 33)1089
Stacking modelsLSTM-BiBidirectional (LSTM (64))(None, 41, 128)3379278,177
Bidirectional (LSTM (32))(None, 64)41216
Dense(None, 32)2080
Dense(None, 33)1089
LTSM-StackLSTM(None, 41, 256)264192525,281
LSTM(None, 41, 128)197120
LSTM(None, 41, 64)49408
LSTM(None, 32)12416
Dense(None, 32)1056
Dense(None, 33)1089

Space-time modelConvLSTMConvLSTM2D(None, 41, 1, 1, 41)62156117,233
BN(None, 41, 1, 1, 41)164
Flatten(None, 1681)0
Dense(None, 32)53824
Dense(None, 33)1089