Real-Time Communication-Aware Ride-Sharing Route Planning for Urban Air Mobility: A Multi-Source Hybrid Attention Reinforcement Learning Approach

2025-07-18

AI Summary

Addressing the dual challenges of dynamic demand response and communication quality assurance in real-time shared mobility path planning for Urban Air Mobility (UAM), this paper proposes a Multi-Source Hybrid Attention Reinforcement Learning (MSHA-RL) framework. MSHA-RL innovatively integrates radio map modeling with multi-source heterogeneous state representation, and introduces a hybrid attention mechanism to align high-dimensional, heterogeneous passenger requests and vehicle states—enabling joint perception and decision-making under global airspace communication constraints and local dynamic environments. Experimental results demonstrate that, compared to baseline methods, MSHA-RL significantly reduces average trip time by 23.6%, improves communication compliance rate by 92.4%, and enhances real-time responsiveness to emergent requests as well as the safety and quality of generated trajectories.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Multiagent Systems: Multiagent Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

Abstract

Urban Air Mobility (UAM) systems are rapidly emerging as promising solutions to alleviate urban congestion, with path planning becoming a key focus area. Unlike ground transportation, UAM trajectory planning has to prioritize communication quality for accurate location tracking in constantly changing environments to ensure safety. Meanwhile, a UAM system, serving as an air taxi, requires adaptive planning to respond to real-time passenger requests, especially in ride-sharing scenarios where passenger demands are unpredictable and dynamic. However, conventional trajectory planning strategies based on predefined routes lack the flexibility to meet varied passenger ride demands. To address these challenges, this work first proposes constructing a radio map to evaluate the communication quality of urban airspace. Building on this, we introduce a novel Multi-Source Hybrid Attention Reinforcement Learning (MSHA-RL) framework for the challenge of effectively focusing on passengers and UAM locations, which arises from the significant dimensional disparity between the representations. This model first generates the alignment among diverse data sources with large gap dimensions before employing hybrid attention to balance global and local insights, thereby facilitating responsive, real-time path planning. Extensive experimental results demonstrate that the approach enables communication-compliant trajectory planning, reducing travel time and enhancing operational efficiency while prioritizing passenger safety.

Problem

• Ensuring communication quality for safe UAM trajectory planning
• Adapting to dynamic passenger demands in ride-sharing scenarios
• Balancing global and local insights for real-time path planning

Innovation

• Radio map evaluates urban airspace communication quality
• Multi-Source Hybrid Attention Reinforcement Learning framework
• Hybrid attention balances global and local insights