AI-Driven Adaptive Air Transit Network with Modular Aerial Pods
Amir Shafiee, Alireza Yazdiani, Hanieh Rastegar, Rui Li, Rayan Karim, Aolei Cao, Ziyang Li, Xieqing Yu, Charlle Sy, Zhaoyao Bao, Xi Cheng, H. Oliver Gao
Published: 2025/9/16
Abstract
This paper presents an adaptive air transit network leveraging modular aerial pods and artificial intelligence (AI) to address urban mobility challenges. Passenger demand, forecasted from AI models, serves as input parameters for a Mixed-Integer Nonlinear Programming (MINLP) optimization model that dynamically adjusts pod dispatch schedules and train lengths in response to demand variations. The results reveal a complex interplay of factors, including demand levels, headway bounds, train configurations, and fleet sizes, which collectively influence network performance and service quality. The proposed system demonstrates the importance of dynamic adjustments, where modularity mitigates capacity bottlenecks and improves operational efficiency. Additionally, the framework enhances energy efficiency and optimizes resource utilization through flexible and adaptive scheduling. This framework provides a foundation for a responsive and sustainable urban air mobility solution, supporting the shift from static planning to agile, data-driven operations.