This paper studies the selection of apron in-ground pits locations, using the Analytic Hierarchy Process to identify key factors influencing the selection of apron in-ground pits locations. It establishes a multi-objective optimization model for apron in-ground pits locations based on utility tunnel technology, considering the construction distance cost, the human resource cost, and the in-ground pits installation cost. The model is validated through simulation using particle swarm optimization algorithm. The results show that the proposed model can achieve the integration of apron in-ground pits. Compared with the existing pit system at Zhuhai Airport, the total cost has reduced by approximately 50.4%, 52.97%, and 34.01% for the three different aircraft parking stand layouts, respectively. This can provide guidance for the application of utility tunnel in airport construction, promote the improvement of apron operation efficiency and unmanned apron realization, and contribute positively to the development of green airports.

As more and more airports are becoming congested, airport operators and airlines are increasingly turning their attention to connecting rail links in a quest to relieve congestion. This paper classifies air-rail links into shared and dedicated connections according to the service range, but the former shows some limitations in terms of travel time. By analyzing the strengths, weaknesses, opportunities and threats of air-rail links abroad, we conclude that air-rail links can be an efficient way to address ground transportation problems. In addition, it can be used as an essential tool to increase an airport's catchment area. Finally, with the introduction of the development situations of air-rail links in large-scale domestic airports, the authors argue that it's necessary to specifically analyze characteristics of the ground access, and adopt different rail links concerning the different conditions of airports. And then the possible use of rail links in Xi'an Xianyang International Airport (XIY) is discussed.

The increasing density and complexity of airport surface operations have intensified the risk of crossing conflicts between ground service vehicles and taxiing aircraft. Such interactions are characterized by strong spatiotemporal coupling, asymmetric right-of-way relationships, and stringent safety requirements, making conventional human-driven conflict avoidance highly dependent on drivers’ perception and judgment. To address this problem, this study proposes a spatiotemporal prediction-driven model predictive control (MPC) framework for autonomous ground vehicles on airport surfaces. First, the spatial interaction between the aircraft safety boundary and the vehicle service road is modeled to define the vehicle–aircraft conflict zone. Aircraft motion information is then used to predict the temporal occupancy of the conflict zone, based on which a dynamic time-window constraint is constructed to characterize the time-varying safe passage conditions for autonomous vehicles. The predicted spatiotemporal constraints are embedded into a rolling MPC framework that continuously optimizes vehicle motion while jointly considering safety, traffic efficiency, and energy consumption. Simulation results show that, compared with human-driven vehicles, the proposed method reduces average energy consumption from 1200.16 kJ to 866.67 kJ and shortens average arrival time from 43.63 s to 41.27 s. In addition, the method demonstrates effective disturbance compensation under aircraft-state uncertainty and adaptability to sequential multi-aircraft crossing scenarios.