Heterogeneous Vertiport Selection Optimization for On-Demand Air Taxi Services: A Deep Reinforcement Learning Approach

2026-01-29

AI Summary

This study addresses the lack of systematic optimization for passenger-optimal routing in existing integrated air-ground mobility systems, which hinders efficient and seamless door-to-door travel. To bridge this gap, we propose UAGMC—the first unified framework for air-ground collaborative mobility optimization—that synergistically integrates deep reinforcement learning with V2X communication to model a multimodal transportation network. Our approach enables intelligent selection of heterogeneous vertiports and dynamic route planning for air taxis by leveraging real-time traffic state awareness and passenger behavior modeling. Evaluated against conventional proportional assignment strategies, UAGMC reduces average travel time by 34%, substantially enhancing the efficiency of integrated air-ground mobility services.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Multiagent Planning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Urban Air Mobility (UAM) has emerged as a transformative solution to alleviate urban congestion by utilizing low-altitude airspace, thereby reducing pressure on ground transportation networks. To enable truly efficient and seamless door-to-door travel experiences, UAM requires close integration with existing ground transportation infrastructure. However, current research on optimal integrated routing strategies for passengers in air-ground mobility systems remains limited, with a lack of systematic exploration.To address this gap, we first propose a unified optimization model that integrates strategy selection for both air and ground transportation. This model captures the dynamic characteristics of multimodal transport networks and incorporates real-time traffic conditions alongside passenger decision-making behavior. Building on this model, we propose a Unified Air-Ground Mobility Coordination (UAGMC) framework, which leverages deep reinforcement learning (RL) and Vehicle-to-Everything (V2X) communication to optimize vertiport selection and dynamically plan air taxi routes. Experimental results demonstrate that UAGMC achieves a 34\% reduction in average travel time compared to conventional proportional allocation methods, enhancing overall travel efficiency and providing novel insights into the integration and optimization of multimodal transportation systems. This work lays a solid foundation for advancing intelligent urban mobility solutions through the coordination of air and ground transportation modes. The related code can be found at https://github.com/Traffic-Alpha/UAGMC.

Problem

• Urban Air Mobility
• vertiport selection
• air-ground integration
• multimodal transportation
• on-demand air taxi

Innovation

• Deep Reinforcement Learning
• Urban Air Mobility
• Vertiport Selection
• Multimodal Transportation
• V2X Communication