# Airport Field Path Optimization Method Based on Conflict Hotspot Avoidance Mechanism
## 1 Introduction
established the three main conflicts of crossover, head-to-head, and tail-following in the taxiing process of the field aircraft in the airport dynamics model; determined the shortest path set using the Yen algorithm based on the topological network, conflict delays, and field taxi space distribution; and optimized aircraft taxi paths according to environmental parameters and aircraft type differences [24]. There are also some scholars who use different dynamic priority strategies to achieve conflict-free taxi path planning for aircraft. In 2015, [PERSON] et al. proposed a Petri net model based on colored taxiways, which adjusts the taxi paths of aircraft in real time by lowering the priority of delayed aircraft entering the airport roadway under the condition of guaranteeing conflict-free taxiing, to avoid a long waiting time [25]. In 2020, [PERSON] et al. implemented a conflict detection and resolution algorithm by simulating the movement process of aircraft in the airport field through a first-come-first-served planner, using three priority strategies and comparing the number of delayed aircraft and the overall delay for conflict resolution [26]. In 2021, [PERSON] et al. constructed a taxiway scheduling optimization model based on the control of time richness, which aimed to improve the punctuality of the arrival of aircraft at the endpoint and used a biogeography algorithm for validation, effectively reducing the number of aircraft taxiing conflicts and lowering the error of arriving at the taxiing terminal [27]. As the focus of research has shifted from standby conflict resolution to the improvement of overall operational efficiency, academics have begun to notice that conflict exhibits spatial clustering in run-slip networks. This space is known as a conflict hotspot. Therefore, the following definition can be derived: a conflict hotspot refers to a subregion of the running skidding system whose conflict probability or related risk index exceeds a certain threshold in a given time window and is spatially and continuously aggregated. Its formation is related to the coupling of traffic flow density, flow direction interweaving, control strategy, and machine-vehicle mixing, etc., which has significant spatial and temporal dynamics, and some scholars try to incorporate it into the study of path optimization in conflict avoidance. For example, in 2015, [PERSON] et al. deeply analyzed the spatial and temporal distribution characteristics of airport hotspots, designed an aircraft taxi avoidance mechanism, and established an aircraft taxi path optimization model to optimize aircraft taxi paths to avoid potential taxi conflicts and effectively reduce the risk level of the hotspot areas [28]. In 2017, [PERSON] et al. constructed an airport node-segment network topology model, established a taxi path optimization model to minimize aircraft taxiing time, and established a taxi avoidance mechanism based on airport ground hotspots to effectively avoid taxing conflicts and reduce the total taxiing time [29]. In 2018, [PERSON] designed a selection avoidance mechanism based on the information on the conflict points, established a taxi path optimization model to minimize the aircraft taxiing time, and used a two-phase path planning algorithm to obtain the optimized path of the aircraft [30]. In summary, most of the current studies on airport field conflicts take aircraft as the main detection object to analyze microscopic conflicts and seldom include the impact of security vehicles in the scope of conflict risk analysis, and there are fewer studies on the comprehensive evaluation of airport conflict hotspots by integrating the activity targets of aircraft and security vehicles.