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

arXiv: https://arxiv.org/abs/2507.14249 · HTML: https://mlsanity.com/paper/2507.14249/

## Key facts

- Cited by 1 papers in the map (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 9 of its 34 references that are in the map.
- First posted on arXiv 18 Jul 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [SG-Former: Self-guided Transformer with Evolving Token Reallocation](https://mlsanity.com/paper/2308.12216/) (2023)
- [Simultaneous Navigation and Radio Mapping for Cellular-Connected UAV with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.07574/) (2020)
- [Radio Map Based 3D Path Planning for Cellular-Connected UAV](https://mlsanity.com/paper/1912.00021/) (2019)
- [Air taxi service for urban mobility: A critical review of recent developments, future challenges, and opportunities](https://mlsanity.com/paper/2103.01768/) (2021)
- [Machine Learning for Wireless Connectivity and Security of Cellular-Connected UAVs](https://mlsanity.com/paper/1804.05348/) (2018)
- [Radio-Map-Based Robust Positioning Optimization for UAV-Enabled Wireless Power Transfer](https://mlsanity.com/paper/1905.12394/) (2019)
- [Radio Map Based Path Planning for Cellular-Connected UAV](https://mlsanity.com/paper/1905.05046/) (2019)
- [Power Efficient Trajectory Optimization for the Cellular-Connected Aerial Vehicles](https://mlsanity.com/paper/1906.09520/) (2019)

## Built on by (most cited first, 1 of 1)

- [Heterogeneous Vertiport Selection Optimization for On-Demand Air Taxi Services: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2601.21316/) (2026)

## Abstract (arXiv)

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.