## 4 Methodology
### Machine Learning Models
#### 4.1.4 Fully Connected Neural Network  
Figure 22 is a flowchart of the construction of a fully connected neural network model for the binary classification of traffic black spot identification. Figure 22 consists of an input layer, a hidden layer, and an output layer, where x represents the feature attributes and y represents the classification results of traffic black spots. A fully connected neural network model is constructed, and data are fed into the model to train the model for the classification task. A fully connected neural network can consist of multiple hidden layers, each layer containing multiple neurons and each neuron being connected to all the neurons in the previous layer. Some parameters need to be set during training, and after network tuning, some of the hyperparameters of the fully connected neural network proposed in this study are set at the following values: \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Name** & **Retrieve a Value** & **Clarification** \\ \hline bootstrap & True & Indicates sampling with put-back \\ \hline max\_depth & 13 & Growth depth of each decision tree \\ \hline n\_estimators & 15 & Number of decision trees \\ \hline learning\_rate & 0.05 & Learning rate \\ \hline \hline \end{tabular}
\end{table} 
Table 19: Values of some important parameters of random forest. Figure 21: Random forest grid search tuning parameter map. 1. Input layer vector dimension: 139 features of each intersection semantic segmentation will be input;
2. Number of hidden layers: the number of hidden layers is three;
3. Neurons: the neurons of the three hidden layers are 79, 17, and four, respectively;
4. Activation function: the activation function is chosen as ReLU, which is less likely to have gradient explosion or gradient disappearance;
5. Learning rate: the learning rate is between 0.005 and 0.01;
6. Back-propagation algorithm: after the neural network calculates the error according to the error function in the process of iteration. It needs to return the result to the end of input weight adjustment, which needs to consider the model effect and speed. In this study, [PERSON]'s algorithm is chosen as the back-propagation algorithm for this model. The values of some parameter settings of the fully connected neural network are shown in Table 20.  
Traffic Black Spot Recognition Model Construction Based on Graph Convolutional Neural Network Model (GCNN)  
In this experiment, roads can be considered as nodes of the graph, and connections between neighboring roads are considered as edges of the graph. Then, the road features from the street view image data source can be input into the GCNN as node features. With the GCNN model, the following operations can be performed:  
1. Neighbor node feature aggregation: the GCNN model can fuse the information between adjacent roads by aggregating the neighbor node features of each node. This allows for the features of neighboring roads to be used to better portray the urban built environment; \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Name** & **Grade** & **Value** \\ \hline Output Layer Vector Dimension & \(n\) & 139 \\ \hline Number of hidden layers & \(l\) & 3 \\ \hline Hidden layer neurons & \(o\) & 79, 17, 4 \\ \hline Activation function & F(\(x\)) & ReLu \\ \hline Learning rate & \(\eta\) & 0.0001 \\ \hline Algorithm & \(A\) & Adam \\ \hline \hline \end{tabular}
\end{table} 
Table 20: Hyperparameter settings for fully connected neural networks. Figure 22: The process of constructing a classification model for traffic black spot recognition by a fully connected neural network. 2. Contextual information learning: the GCNN model can learn the contextual information of each node, including the attributes, shapes, and traffic flows of neighboring roads. This helps to synthesize the relationship between roads to further portray the urban built environment;
3. Feature representation learning: the GCNN model can learn the representation of road features through multi-layer convolutional operations.