## 5 Model Evaluation  
Five machine learning systems were evaluated using the datasets described above. These included BiLSTM, Uni-LSTM, RNNs, Elman, and DLBP neural networks. These models have been widely used for traffic forecasts, and they were implemented using MATLAB R2020b and Neuralware Professional [68].  
This study collected traffic data across all lanes of Hoddle Street during weekdays and weekends and under varying traffic conditions. The data were categorised based on time of day (e.g., peak hours, off-peak hours) and specific traffic conditions (e.g., free-flowing, congested), allowing the model to account for different patterns in traffic behaviour. Lane-specific data were also integrated, capturing variations in traffic flow across different lanes of the arterial road.  
For clarity, the following variables were included in the model:  
* Traffic Flow: Measured as the number of vehicles passing a specific point per unit of time.
* Traffic Speed: The average speed of vehicles at each measurement point.
* Time of Day: Categorised as peak or off-peak hours.  
These variables were carefully defined and included as input features to ensure that the model could adapt to the dynamic nature of traffic on the arterial road.  
In this study, the primary variables for predictions are traffic speed and volume, the dependent variables in the LSTM and BiLSTM models. It is acknowledged that the spatial location of the measurement points along the arterial road is an important factor influencing these variables; in this case, they were fixed at signalised intersections.  
First, the data were arranged as two-column values. The first column corresponds to speed/volume at time t, and the second column corresponds to the expected output (t + n), where n ranges from 15 min to 60 min into the future. Then, the data were partitioned into training and testing sets. The models were trained on the first 60% of the sequence and tested on the last 40%. To prevent model overfitting, the training/testing data were standardised to have zero mean and unit variance. The DLPB model parameters used for this experiment included three hidden layers with 4, 6, and 2 neurons. The transfer function was Tanh with a learning coefficient output (\(\alpha\) = 0.15). The learning rule was Ext DBD with 100,000 iterations and a momentum of 0.4. RNN and Elman's models were created using one hidden layer with ten neurons, activation function (tanh), learn rule (ext DBD), and [1:2] layers delay. The BiLSTM and Uni-LSTM networks were created using four layers: sequence input layer (number of features = 1), Uni-LSTM/BiLSTM layers (number of hidden units = 300), fully connected layer (number of responses = 1), and regression layer. The model hyperparameter settings are presented in Table 2. The experiments were also  
Figure 6: Uni-LSTM/BiLSTM Architecture (source: author).  
implemented with the Deep Learning Toolbox functions of trainNetwork, training options, and predictAndUpdateState using the following hyperparameters [69; 70].  
A total of four experiments were tested with the model. The first two experiments predicted speed and flow traffic conditions for up to 1 h using only flow and speed information as inputs (Figure 7). The other two experiments explored the impacts of multiple weather data on traffic flow and speed prediction for up to 1 h into the future (Figure 8). See the figures below for architecture and model representation.