## 3 Methods  
### BP Neural Network  
The backpropagation technique was used to train the BP, a multilayer feedforward neural network [8]. As illustrated in Figure 2, the BP neural network structure consists of an input layer, one or more hidden layers, and an output layer. BP's main benefit is its capacity to pick up intricate nonlinear input-output mappings. The gradient-based optimization underpinning the BP learning process involves iteratively adjusting the weights and biases to minimize the cross-entropy loss. In order to update parameters, the gradients [9] of the loss function are transmitted backward through  
Figure 1: The six collection sites. (**A,B**) Primary school sites without sidewalks; (**C–F**) Middle school sites with sidewalks. Figure 2: The Process of BP Neural Network. the network using the steepest descent approach. Because it measures the discrepancy between the actual label distribution and the expected probability distribution, cross-entropy loss is very useful for classification problems. Figure 3 shows the dataset used in this investigation, which has twelve input features and one output label. The BP model captures the link between these features and the desired result [10]. We use an adaptive learning rate technique and batch normalization to improve training stability and convergence speed. The dataset employed in this study comprises twelve input features and one target label. Table 1 provides a detailed description of each feature and the corresponding label. The BP neural network model, illustrated in Figure 3, is structured with an input layer, three hidden layers, and an output layer designed to effectively capture the relationship between the input features and the target output. Equation (1) gives the calculation formula of the BP neural network from the input vector X to the output vector Y.  
\[O_{l}=g\{\sum_{k=1}^{v}w_{kl}\times f[\sum_{j=1}^{n}w_{jk}\times f(\sum_{i=1}^{ m}x_{i}\times w_{ij}+b_{1j})+b_{2k}]+b_{3l}\},l=1,2,\ldots,T \tag{1}\]  
This formula represents a neural network with three hidden layers, where each output \(O_{l}\) is computed through a series of weighted sums and activation functions. The input values \(x_{i}\) are combined with weights \(w_{ij}\) and a bias term \(b_{1j}\), then passed through an activation function \(f\), forming the first hidden layer. The results from this layer are further processed by another set of weights \(w_{jk}\) and biases \(b_{2k}\), again undergoing the activation function to create the second hidden layer. Similarly, these values propagate through the third layer using weights \(w_{kl}\) and biases \(b_{3l}\), before being transformed by the final activation function g to produce the output \(O_{l}\). The parameters \(m\), \(n\), and \(v\) define the number of neurons in each respective layer, while T represents the total number of output neurons. \[\text{f(x)}=\frac{1}{1+e^{-x}},x\in(0,1) \tag{2}\]  
Figure 3: BP Neural Network Model. Equation (2) represents the activation function. This formula defines the sigmoid activation function, which maps an input \(x\) to an output within the range (0, 1). Where \(e\) is the base of the natural logarithm. The input variable \(x\) determines the function's output, with larger values pushing the result closer to 1 and smaller values bringing it closer to 0. This nonlinear transformation enables the function to introduce smooth and differentiable behavior, making it widely used in neural networks for binary classification and gradient-based optimization. \[L=-[y\cdot\log(\hat{y})+(1-y)\cdot\log(1-\hat{y})] \tag{3}\]  
Equation (3) represents the binary cross-entropy loss function, which measures the discrepancy between the true binary labels and the predicted probabilities. In this equation, \(L\) denotes the loss value, \(y\) is the ground truth label that takes a value of either 0 or 1, and \(\hat{y}\) represents the predicted probability that the given sample belongs to the positive class.