To improve the efficiency of urban last-mile logistics distribution and promote the development of the low-altitude economy, this study proposes a site location method for hierarchical UAV takeoff and landing sites tailored to urban low-altitude logistics demands. First, a conditional Logit model was utilized to identify key influencing factors and evaluate urban low-altitude logistics demand. Second, the polygon intersection point set method for multi-level facilities was applied to determine the candidateset of UAV takeoff and landing sites. Finally, a multi-objective maximal coverage location model for hierarchical UAV takeoff and landing sites was established, with optimization objectives of maximizing demand coverage and minimizing construction costs and the rate of redundant demand coverage. An improved NSGA-Ⅲ algorithm was designed to solve the model. The results show that the performance of facilities is optimal when the number of vertiport sites and points respectively reach 53 and 107 in Shanghai, with a demand coverage rate of 85.6%, construction costs amounting to 9.59 million CNY, and a redundant demand coverage rate of 33.7%. The location scheme demonstrates high rationality and feasibility. In addition, the study reveals that economic development level and transportation accessibility significantly influence the spatial distribution of UAV takeoff and landing sites. Setting sites in areas with high GDP and abundant transportation facilities can further enhance demand coverage, although it may lead to increased construction costs and service range redundancy. The research can provide scientific decision-making support for the planning and implementation of urban low-altitude logistics systems.

The aviation transportation industry is one of the largest industries of carbon emission. With the development of the aviation transportation industry and the strengthening of environmental regulations, the industry is facing enormous environmental pressure. How to reduce the carbon emissions of the air transport industry has attracted wide attention. Aircrafts taxiing in airport scene emits a lot of carbon. Therefor, optimizing aircraft taxiing is an important way to reduce aviation transportation carbon emissions. In addition, to ensure the safety and efficiency of aviation transportation, taxiing on the airport apron must also consider factors such as taxiing waiting time and taxiing conflicts. To solve this problem, this paper proposed an optimization model of space-time taxiing of aircraft considering carbon emission. By deciding the taxiing path in the upper model and the taxiing time in the lower model, the upper and lower interactive optimization minimizes the total carbon emission of aircraft taxiing, the taxiing waiting time of approach aircrafts and the conflict of aircraft taxiing. To improve the quality of the model solutions, an iterative neighborhood search algorithm based on the interaction optimization of upper and lower models was designed to solve the model and the conflict-free neighborhood operator and stochastic perturbation operator were introduced. The proposed model and algorithm were validated with actual operational data from Guangzhou Baiyun International Airport. The experimental results show that compared with the shortest taxiing strategy, the proposed model reduced the total carbon emission for 47 aircrafts by 971 kg, accounting for 1.8% of the total carbon emissions, in the planning period of one and a half hours under the condition of avoiding aircraft taxiing conflict. This research can provide a more environmentally friendly taxiing strategy while improving taxiing efficiency and safety, helping the aviation transportation industry reduce carbon emissions and achieve greater sustainability.

The coordination restrictions of several apron support services and dynamic flight information have resulted in increased operational demand on large airport apron support vehicles and more difficulties with vehicle scheduling. Considering the difference of vehicle operation constraints in three different modes of continuous operation, capacity-limited continuous operation and round-trip operation, a multi-vehicle cooperative apron support vehicle scheduling model is established with the goal of minimizing the number of vehicles and the total driving distance. The model is solved in two stages according to the actual operation of the airport. In the static stage, a local search non-dominated corting genetic algorithms Ⅱ (LS-NSGA II) algorithm integrating local search is designed. In the dynamic stage, a similar neighborhood search algorithm is designed. The static results show that the number of vehicles and the total driving distance are reduced by 18.9% and 8.9% respectively compared with the first-come-first-served. In comparison to the big neighborhood search technique, the dynamic results can keep the number of cars in the static scheduling plan while reducing the adjusted driving distance by 25.8%. The research results can provide a certain guiding significance for the scheduling management and decision-making of large airport apron support vehicles.