Automated Airspace Optimization for UAM Operations via Hyperparameterized Legal-Framework Analysis and Predictive Compliance Scoring

**Abstract:** This research proposes a novel system for automated airspace optimization for Urban Air Mobility (UAM) operations, leveraging a hybrid approach that combines legal framework analysis with predictive compliance scoring. Utilizing a multi-layered evaluation pipeline, the system analyzes evolving regulatory landscapes (specifically within the context of UAM 운항을 위한 공역 법제 연구), identifies potential conflicts, and proactively optimizes flight paths and operational procedures to maximize airspace utilization while maintaining full legal compliance. This approach mitigates the risks associated with regulatory ambiguity and dynamically adapts to the evolving legal context of UAM, offering a scalable and commercially viable solution for airspace management authorities and UAM operators. The core innovation lies in the incorporation of legally-informed hyperparameterization, enabling the system to learn and adapt to the nuanced requirements of the regulatory environment, vastly improving operational efficiency and safety compared to rule-based approaches. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*