## 5 Experimental Results and Analysis  
### Indicators for Model Evaluation  
The data partitioning is kept consistent for all machine learning models and is divided into a training set and a test set. This ensures the comparability of the experimental results. When constructing the algorithmic model, all the sample data (a total of 2519 entries, each with 139 features) in the two categories (traffic black spots and non-traffic black spots) are randomly divided into 80% as the training set and 20% as the test set.  
The main evaluation metrics commonly used in machine learning classification models are the following: (i) Accuracy; (ii) Precision; (iii) Recall; and (iv) F1_score; these metrics can help us evaluate the performance of the algorithm in different situations as well as provide valuable feedback when improving the algorithm. A confusion matrix is a matrix used to evaluate the performance of classification algorithms. In the binary classification problem of traffic black spot recognition at road intersections, the confusion matrix is a 2 \(\times\) 2 matrix that contains the values True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (False Negative, FN).  
1. Definition of \"Accuracy\": the ratio of the number of correctly categorized samples to the total number of samples. Accuracy is one of the most commonly used evaluation indices for classification models, but for the case of data imbalance, the accuracy may be misleading, and the expression is: \[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}}\] (1)
2. Definition of \"Precision\": the proportion of samples classified as positive that are truly positive. Precision is the evaluation of the ability of the classifier, i.e., the proportion of truly positive samples among all the samples determined as positive by the classifier, expressed as: \[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\] (2)
3. Definition of \"Recall\": the proportion of all positive samples that are correctly categorized as positive samples. Recall is an index for evaluating the ability of a classifier to detect positive samples, i.e., the percentage of positive samples that can be detected by the classifier out of all real positive samples, expressed as: \[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\] (3)
4. Definition of F1 Score (F1_score): the F1 value is a metric that combines precision and recall, i.e., the reconciled average of precision and recall. The higher the F1 value, the better the classification performance of the model, and the expression is: \[\text{F1\_score}=\frac{2*\text{Precision }*\text{ Recall}}{\text{Precision }+\text{ Recall}}\] (4)  
In the experiments of this paper, the precision rate represents the proportion of real traffic black spots among the number of all judged traffic black spots; the recall rate represents the ability of the maximization model to find all traffic black spots in the dataset, i.e., the proportion of the number of correctly judged traffic black spots to the proportion of the actual number of all traffic black spots; and the F1 score is a combination of the two metrics of the precision rate and the recall rate, and the higher the value of the F1 score, the better the classification performance of the model.  
The data are randomly assigned and the ratio of data in the training set and test set is 4:1. SVM, GBDT algorithm, random forest algorithm, and fully CNN methods are utilized for training and evaluation of the data.