# Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology
## 3 Mainstream Models and Methods
### Analytical Modeling
#### 3.2.2 Neural Network  
Table 4 provides a summary of the representative literature from the last 10 years that utilizes the neural network method to predict and analyze taxi time, both domestically and internationally. In 2012, [PERSON] et al. [43] developed a taxi time prediction model by employing state data indicators of the airport surface as inputs to the neural network. In 2019, [PERSON] et al. [59] created a prediction model using a combination of gradient boosting trees and neural networks. They further conducted an analysis to determine the importance of feature variables. In 2020, [PERSON] et al. [60] proposed a novel prediction model called spatio-temporal environmental deep learning (STEDL). They considered three major feature variables, namely time, space, and environment, as inputs to the model. The STEDL model consists of three sub-models, namely the time flow sub-model, the spatial sub-model, and the environmental sub-model. Comparatively, this model outperformed other machine learning algorithms in terms of predictive performance. In 2022, [PERSON] [33] extended the consideration of feature variables and classified 12 of them into numerical and categorical variables. Analyzing the magnitude of correlation between these variables and taxi time, they constructed a taxi-out time prediction model based on a deep multilayer feed-forward neural network. To accurately predict flight delays, [PERSON] et al. [71] proposed a vision-based solution that leverages various vehicle trajectories and environmental sensor data in the airport apron area. By constructing a situational awareness map and employing the end-to-end deep learning architecture TrajCNN (Convolutional Neural Network), they captured spatial and temporal information, achieving high-precision forecasting of departure delays. This framework has demonstrated successful results, with a prediction error of approximately 18 min, at Los Angeles International Airport. This is crucial for airlines as traditional methods struggle to accurately predict departure delays. In order to discuss the influence of the strength of the correlation of feature variables on the prediction accuracy of the model, in 2022, [PERSON] et al. [67] proposed a taxi time prediction model based on the BP neural network to analyze the influence of weak, medium, and strong correlation factors on the prediction, and the results show that the 5-element combination model (medium and strong) has the best fit, and the addition of weak correlation factors reduces the accuracy of the prediction. However, the stability and accuracy of the model are not good, and the neural network is not sensitive to the initial thresholds and weights in prediction. Aiming at the above problems, on the basis of previous research, [PERSON] et al. [69] proposed an improved BP neural network method prediction model based on SSA (Sparrow Search Algorithm), which improves the accuracy of model prediction by using the Sparrow Search Algorithm to obtain the optimal thresholds and weights for the neural network. Subsequently, [PERSON] [68] constructed a taxi-out time prediction model based on correlation analysis, classified the influencing factors into significant correlation factors and moderate correlation factors, and also comparatively analyzed the model prediction results based on SVR and BP neural network. The experimental results show that for both models the prediction accuracy of the seven-element combination (significantly correlated factors and moderately correlated factors) model is the best, and the prediction accuracy of the SVR-based model is higher than the prediction accuracy of the BP neural network-based model. Aircraft taxi time is influenced by a myriad of complex and diverse factors, encompassing both linear features such as taxiing distance and nonlinear features such as surface congestion patterns. Due to this complexity, the relationship between taxi time and characteristic variables cannot be entirely explained by a linear or nonlinear model alone.