# Road Passenger Load Probability Prediction and Path Optimization Based on Taxi Trajectory Big Data
## 5 Experimental Results
### BiLSTM Predicts the Probability of Carrying Passengers  
This study employs _BiLSTM_ to predict passenger probability and conducts a comparative analysis of prediction accuracy with BP and _LSTM_ models. Both the hidden and output layers use the ReLU activation function. The design of the _BiLSTM_ and _LSTM_ passenger volume models is similar to DFN, with added dropout regularization to prevent overfitting and a parameter set to 0.5. The number of nodes in the hidden layer is set to 128. The parameter settings of the _BiLSTM_ and _LSTM_ prediction models constructed in this study are shown in Table 4.  
To observe and analyze the models' ability to predict short-term road segment traffic, as well as the differences in predictive capabilities among the models, BP, _LSTM_, and _BiLSTM_ deep learning models were used to predict road segment passenger volume during the evening peak period. Ten experiments were conducted to obtain the average _MSE_. The training and prediction results are shown in Figure 11.  
The mean square error is the expected value of the square of the difference between the estimated value of the parameter and the true value of the parameter, and it is denoted as _MSE_, which can be used as a measure of the degree of variability of the data, which is calculated as follows:  
\[MSE=\frac{1}{n}{\sum_{i=1}^{m}{(y_{i}-\hat{y_{i}})}^{2}} \tag{15}\]  
where \(y_{i}\) is the real data, \(\hat{y_{i}}\) is the fitted data, and \(n\) is the number of samples  
Taking the mean square error as the loss function, the convergence curves of the four cases are obtained, as shown in Figure 12. In the following figures, Figure 12a-c show the data convergence curves for section a of the Second Ring Elevated Road, Figure 12d-f show the data convergence curves for the Chengdu Airport Expressway, Figure 12g-i show the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Setting Parameter** & **Description** & **Setting Value** \\ \hline Number of input nodes & Product of number of features and sliding window setting & 16 \\ Number of output nodes & Predicted road traffic & 1 \\ Number of hidden nodes & 4 hidden layers & 128, 64, 32, 16 \\ Learning rate & Dynamic learning rate: every 50 epochs, the learning rate is & 0.01 \\ Activation function & Set hidden node activation function & ReLU activation function \\ Regular optimization & Dropout regular optimization & Dropout = 0.5 \\ Gradient descent & Batch gradient descent sample value setting & Batchsize = 24 \\ Termination condition & Stop iteration if the change of loss value is less than the set value & \(1\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model parameters. convergence curves for the section of East Street on East Main Street, and Figure 12j-1 show the data convergence curves for section b of the Second Ring Elevated Road. After comparing the model training results, it was found that the BP network model converges significantly slower than the _LSTM_ and _BiLSTM_ networks. The BP network converges at around 250 epochs, while the _LSTM_ and _BiLSTM_ networks converge at around 80 epochs. The _MSE_ loss values are 0.034 for the BP network, 0.033 for the _LSTM_ network, and 0.031 for the _BiLSTM_ network. The _BiLSTM_ deep learning model has a prediction accuracy that is 9.67% higher than the BP deep learning model and 6.45% higher than the _LSTM_ deep learning model. Therefore, the BP network has a slower convergence rate and  
Figure 11: Model training results and prediction results.