# Airport Field Path Optimization Method Based on Conflict Hotspot Avoidance Mechanism
## 1 Introduction  
According to the Civil Aviation Administration of China (CAAC), the aviation industry recorded 841,000 h of transport flights in January 2023, representing a sequential increase of 75.2% and a year-on-year rise of 11.0%. There were also 342,000 flights, reflecting increases of 78.2% sequentially and 10.4% year-on-year. Overall, the sector is experiencing a rapid recovery, with a transport turnover of 7.39 billion ton-kilometers, up 13.9% compared with the previous year. Passenger transport volume reached 39.775 million, marking an increase of 34.8%, while cargo and mail transport saw a decline of 25.1%, totaling 490,000 tons. This indicates a strong rebound toward pre-pandemic levels, although challenges remain in the cargo sector. As flight volumes increase, major airports serving over 10 million passengers are facing heightened operational pressures. To tackle these challenges, a study is being conducted to simulate the environments of busy airports and identify key conflict hotspots. By optimizing aircraft taxiing paths, this research aims to reduce conflicts and improve overall operational efficiency. At present, domestic and international research on airport field aircraft path optimization is mainly divided into two aspects: static path planning and dynamic path planning. Static path planning generates one-time glide routes by calculating time-averaged costs or risks based on historical trajectories or predefined scenarios over a longer period of time, whereas dynamic path planning utilizes real-time radar/ADS-B data, or simulated forecasts, to update paths on a rolling basis and deconflict online, which is more responsiveto the characteristics of traffic flow over time. With the expansion of airports and the increase in airport traffic, the field operation process becomes more and more complex, and the related studies on path optimization also consider more about the influence of field operation rules, field operation conflicts, and control transfer on the path optimization strategy. For static path planning, in 1998, [PERSON] et al. used [PERSON]'s algorithm to optimize airport field aircraft paths based on airport network diagrams, but it lacked consideration of the possibility of dynamic taxi conflicts occurring during the aircraft taxi process [1]. In 2015, [PERSON] et al. proposed an efficient branch delimitation algorithm for the problem of multi-runway aircraft sequencing to optimize the shortest path problem in the presence of time windows, as well as non-triangular separations [2]. In 2018, [PERSON] et al. implemented an aircraft field taxi path planning algorithm using a waitlist model to represent a path planning scheme, aiming to minimize the total taxi time and total waiting time [3]. In 2019, [PERSON] used a genetic algorithm to analyze the taxi cost of a straight-line taxiway under specified weights and used a depth-first traversal algorithm to select and optimize aircraft ground taxi paths based on conflict resolution [4]. In 2020, [PERSON] established a computational model for fuel consumption and pollutant emissions of field taxiing to evaluate the fuel consumption and pollutant emissions during aircraft field taxing and used it as the basis for path optimization [5]. In 2022, [PERSON] et al. used the aircraft field global operation time as the shortest, and proposed a taxiing path planning model based on situational awareness to optimize the aircraft taxiing path [6]. In terms of dynamic path planning, there is more research on optimizing aircraft taxiing paths by improving various path planning algorithms. Among them, some scholars improve the coding method of the genetic algorithm, integrate the glide conflict constraints, and consider the weight coefficients of waiting time to solve the glide optimization model to obtain the optimal glide path of aircraft and improve the efficiency of conflict resolution for field objectives [7; 8].