# Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology
## 2 Classification of Research Perspectives
### Type of Surface Taxi Time
#### 2.1.1 Reference Time
[12] developed a multivariate linear regression model based on historical data, providing estimates for the time it takes a departure aircraft to taxi from the stand to the runway head. [PERSON] [13] proposed an econometric regression model using a lognormal approach, resulting in more accurate predictions of reference time compared to the FAA APO method. In a pioneering effort, [PERSON] et al. [14] considered runway resource utilization and selected the number of aircraft using the same runway for departure and arrival as an independent variable. They established a lognormal regression model to predict reference times for different aircraft types. [PERSON] et al. [15] treated the aircraft departure process as a queuing system. They utilized variables such as pushback schedule, gate location, runway configuration, and weather conditions to construct a regression model predicting the estimated reference time. Their study revealed a significant correlation between the number of departure aircraft in the queue and additional time in taxi-phase. The percentile method is another commonly used approach for estimating reference time due to its simplicity in computation. [PERSON] et al. [16] estimated reference time as a normal random variable, assuming it to be equal to the mean of their estimation. [PERSON] et al. [17] computed the reference time for an aircraft with the same combination of gates, locations, and runways by taking 10 percent of the actual taxi time as the reference time. [PERSON] et al. [18], after analyzing factors influencing aircraft departure taxi time, proposed a measure of surface traffic state. Based on this indicator, they constructed a model for predicting reference time. The actual taxi time of aircraft within the same group with all measures lower than the congestion value is ranked from smallest to largest, and the aircraft ranked within 10-90% is selected. Subsequently, the average taxi time of the selected aircraft is calculated and taken as the reference time for that specific group. Other methods from the literature  
[PERSON] et al. [27] devised a novel model for estimating reference time, utilizing the node-link structure of airports and Airport Surface Detection Equipment (ASDE) ground surveillance data. The data were organized based on taxiing sections, aircraft wake, and aircraft type. By calculating the reference time for each section and then summing them up, the total reference time for the aircraft was determined. [PERSON] et al. [28] conducted a comparative study of three methods for estimating reference time: a statistical regression, a percentile method, and an ASDE data monitoring method. The findings demonstrated that predictive models utilizing monitoring information were more effective and capable of swiftly identifying taxiway locations where delays were most likely to occur. Furthermore, meta-heuristic algorithms have been employed to estimate reference time. [PERSON] et al. [29] developed a meta-heuristic model using the tabu search method and genetic algorithms to calculate the shortest route from the gate to the takeoff point for an aircraft. They also incorporated the aircraft's average taxiing speed in that segment to estimate reference time. By optimizing gate assignments, they successfully minimized additional time in taxi-phase and reference time, contributing to improved efficiency in airport surface operations.