# Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology
## 3 Mainstream Models and Methods
### Simulation Modeling
The above prediction of taxi time based on the queuing model theoretically investigates the estimation method and principle of taxi time, but the model does not have good robustness in terms of the treatment of random factors, the prediction accuracy needs to be improved, and the experimental results differ greatly from the actual ones. In 2002, [PERSON] et al. [75] established a parameter matrix by considering airport throughput, departure congestion, average taxiing delay time, etc., and simulated aircraft departure queuing process through stochastic parameters as a way to predict taxi time under the flow restriction. In 2015, [PERSON] et al. [17] applied the linear optimization sequencing method to develop a discrete-event fast simulation tool, LINOS, to simulate the current airport state and during each run of the simulation, the simulated taxi time of each aircraft is calculated by LINOS. In order to assess the performance level of the method, the prediction results were compared with the results of using a machine learning approach based on historical data and with the results of a reference time-based trajectory projection method, which showed that the model prediction accuracy was comparable to that of the support vector machine method, and outperformed the linear regression algorithm and the reference time based trajectory projection method. This method, which relies on simulation software to predict the taxi time, requires simulation software with very high simulation capability to realize, and the parameters of the software need to be constantly adjusted according to the actual operation status of the surface to realize the simulation requirements. Therefore, although the prediction accuracy of this method is high, it has the disadvantages of high cost and time-consuming adjustment of parameters. In order to dynamically predict the taxi time so as to better control the taxiing process to reduce carbon emissions, in 2019, [PERSON] et al. [77] established a mixed-integer linear programming model containing an uncertain set of taxi times from the perspective of flight sequencing and runway scheduling. The taxi-out time is predicted by simulating the taxiway queue during aircraft departure. In 2020, [PERSON] et al. [27] established an reference time model based on the airport node-link model. The data were categorized according to taxing section, aircraft wake, and flight type; and the total taxi time of the aircraft was obtained by calculating the reference time of each section on the node-link model. In addition, some scholars combined taxi time prediction with taxi route optimization. In 2010, [PERSON] et al. [8] used [PERSON]'s algorithm to solve for the shortest paths on the runway and taxiway operation network, and used mixed-integer linear programming methods to generate flight routes that satisfy a given time window with minimum total delays and no conflicts, and solved for the taxi time by shortest paths. In 2011, [PERSON] et al. [9] considered the airport surface as a network structure of nodes and arcs and comprehensively considered factors such as conflict avoidance, safety intervals, and taxiway operation rules to find the optimal taxi route with the objective of minimizing the sum of taxi time and waiting time by using the branch-and-bound method. In 2018, [PERSON] et al. [10] established a taxi route optimization model based on the conflict-point selection and avoidance mechanism and used an heuristic algorithm to solve the optimal taxi route with the objective of minimizing the taxi time by using the heuristic algorithm for dynamic and static planning of taxi routes. The experimental results show that the algorithm can reduce the conflicts of aircraft on the optimal taxi route in order to reduce the taxi time.