mlsanityData to 14 Oct 2026

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

arXiv 2601.21316 ↗First posted 29 Jan 2026cs.LG cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 15 of its 81 references that are in the map. First posted on arXiv 29 Jan 2026.

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15 of 81 references.

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  1. 2025Real-Time Communication-Aware Ride-Sharing Route Planning for Urban Air Mobility: A Multi-Source Hybrid Attention Reinforcement Learning Approach18 Jul

Other references · 14

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Reinforcement Learning for Solving the Vehicle Routing Problemcited by 333
  3. 2019Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Controlcited by 164
  4. 2018Learning Scheduling Algorithms for Data Processing Clusterscited by 134
  5. 2019Dealing with Sparse Rewards in Reinforcement Learningcited by 42
  6. 2020Queueing Network Controls via Deep Reinforcement Learningcited by 41
  7. 2024LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban Environmentscited by 31
  8. 2023UniTSA: A Universal Reinforcement Learning Framework for V2X Traffic Signal Controlcited by 13
  9. 2023Sample Efficient Reinforcement Learning in Mixed Systems through Augmented Samples and Its Applications to Queueing Networkscited by 9
  10. 2021Air taxi service for urban mobility: A critical review of recent developments, future challenges, and opportunitiescited by 7
  11. 2024Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systemscited by 7
  12. 2021Predicting Demand for Air Taxi Urban Aviation Services using Machine Learning Algorithmscited by 3
2 more
  1. 2021Ridesharing and Fleet Sizing For On-Demand Multimodal Transit Systemscited by 1
  2. 2021Passenger-Centric Urban Air Mobility: Fairness Trade-Offs and Operational Efficiencycited by 1

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  3. 2024Graph Learning-based Fleet Scheduling for Urban Air Mobility under Operational Constraints, Varying Demand & Uncertainties
  4. 2025Intelligent Offloading in Vehicular Edge Computing: A Comprehensive Review of Deep Reinforcement Learning Approaches and Architectures
  5. 2026Value-Decomposed Reinforcement Learning Framework for Taxiway Routing with Hierarchical Conflict-Aware Observations
  6. 2024A Graph-based Adversarial Imitation Learning Framework for Reliable & Realtime Fleet Scheduling in Urban Air Mobility
  7. 2025Integrated Noise and Safety Management in UAM via A Unified Reinforcement Learning Framework
  8. 2026AoI-Guaranteed Dynamic Route Planning for Connected Vehicles ↗