# Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology
## 1 Introduction  
The complex layout of large airports, dense traffic flow, and random external environmental disturbances often lead to airport congestion and flight delays. Congestion is primarily a result of either an overly dense distribution of aircraft on the surface or the airport's current relatively low operating capacity, which is unable to meet the high traffic demand generated by actual operations. Currently, there are two solutions to address the airport congestion problem. Firstly, physical measures to expand the available resources of the airport can be taken. This can be achieved by increasing the number of stands, expanding the terminal building, and adding new runways and taxiways, among other strategies. However, these physical measures come with challenges, as they require significant investments and have long completion cycles, making their implementation difficult. Alternatively, planning measures can be employed; these focus on reasonable control or scheduling of factors during airport surface operations, i.e., the safe and efficient deployment of aircraft operations on the field. Unlike physical measures, planning measures offer a more practical approach to increase the relative capacity and available resources of airports. By carefully planning critical aspects of the aircraft arrival and departure process, these measures can effectively enhance airport operations [1]. The basis of the deployment decision lies in the prediction of the taxi time, and accurate prediction enables effective and efficient deployment. Surface taxi time refers to the total operating time between the airport runway and aircraft stands for arrivals and departures; this serves as a crucial metric to assess airport surface operation efficiency. The airport movement area is a complex network comprising stands, taxiways, runways, and the personnel and vehicles involved in flight services [2]. Upon arrival, an aircraft follows a designated taxi route, guided by air traffic controllers, to reach its assigned stand. Similarly, for departures, aircraft are pushed back from the stands by tractors, they then taxi to the runway end, join the runway queue, and finally take off. This interconnects and constrains the runway system, taxiway system, and aircraft stands assignment system, forming a cohesive spatial link in aircraft operations [3]. Therefore, accurate prediction of surface taxi time can significantly reduce uncertainty in airport surface operations, optimize aircraft scheduling, and enhance airport surface controllability and efficiency. Moreover, precise taxi time prediction plays a pivotal role in aircraft pushback control [3; 4; 5; 6], taxi route optimization [7; 8; 9; 10], runway scheduling, stand assignment optimization [5; 11], and other essential aspects of aircraft arrival and departure processes. As we pursue the advancement of intelligent civil aviation and smart airports, the scientific and accurate prediction of aircraft surface taxi time gains tremendous importance in reducing uncertainty in airport surface operations. This improvement can enhance the level of control and decision-making capabilities of airport surface operations while contributing to reduced fuel consumption and emissions from aircraft. This paper focuses on addressing the challenge of predicting airport surface taxi time. It conducts a comprehensive review of the current state of domestic and international research from various angles, including movement type, taxi time type, and modeling method. By summarizing the existing mainstream prediction models and methods, it aims to shed light on future research directions in this domain. The ultimate goal is to offer valuable methodological guidance and references to researchers in this field. The rest of the paper is organized as follows. Section 2 categorizes taxi time research perspectives in terms of both research objects and research methods. Section 3 focuses on the modeling methods of surface taxi time and reviews the mainstream models and methods.