# Weather-Aware Transformer for Real-Time Route Optimization in Drone-as-a-Service Operations

arXiv: https://arxiv.org/abs/2601.03376 · HTML: https://mlsanity.com/paper/2601.03376/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 21 references that are in the map.
- First posted on arXiv 6 Jan 2026.

## Builds on (most cited first, 2 of 2)

- [DRL4Route: A Deep Reinforcement Learning Framework for Pick-up and Delivery Route Prediction](https://mlsanity.com/paper/2307.16246/) (2023)
- [A Machine Learning Approach to Air Traffic Route Choice Modelling](https://mlsanity.com/paper/1802.06588/) (2018)

## Abstract (arXiv)

This paper presents a novel framework to accelerate route prediction in Drone-as-a-Service operations through weather-aware deep learning models. While classical path-planning algorithms, such as A* and Dijkstra, provide optimal solutions, their computational complexity limits real-time applicability in dynamic environments. We address this limitation by training machine learning and deep learning models on synthetic datasets generated from classical algorithm simulations. Our approach incorporates transformer-based and attention-based architectures that utilize weather heuristics to predict optimal next-node selections while accounting for meteorological conditions affecting drone operations. The attention mechanisms dynamically weight environmental factors including wind patterns, wind bearing, and temperature to enhance routing decisions under adverse weather conditions. Experimental results demonstrate that our weather-aware models achieve significant computational speedup over traditional algorithms while maintaining route optimization performance, with transformer-based architectures showing superior adaptation to dynamic environmental constraints. The proposed framework enables real-time, weather-responsive route optimization for large-scale DaaS operations, representing a substantial advancement in the efficiency and safety of autonomous drone systems.