Integrated scheduling optimization for air taxi service

With the heavy urban traffic congestion, urban low-altitude resources, which have yet to be fully exploited, show great potential to mitigate the traffic congestion problem on the ground to some extent. This paper investigates the emerging mode of urban air mobility and focuses on the operational aspects of air taxis from the perspective of operators. Given the characteristics inherent to both taxi service and traditional civil aviation, an air taxi operational scenario is established. This scenario encompasses a comprehensive consideration of three pivotal facts: flight scheduling, fleet assignment, and routing. Subsequently, air taxi operation mathematical model is formulated. The taxi data of New York City is used as a case study and the problem is solved by a commercial solver Gurobi. We propose an improved genetic algorithm to solve the problem. Compared with Gurobi, the improved genetic algorithm is significantly more efficient and more suitable for application in large-scale problem solving. Finally, a sensitivity analysis of various operational parameters is conducted to assess their respective magnitudes of influence on real-world operations.