Noise-Aware and Equitable Urban Air Traffic Management: An Optimization Approach

TL;DR

This work tackles the challenge of integrating urban air mobility (UAM) by mitigating community noise while maintaining service levels and energy efficiency. It proposes a holistic, noise-aware optimization framework on a three-layer UAM network, combining a hybrid ambient-noise masking strategy with a social-welfare objective to balance demand fulfillment and equity. The model jointly optimizes flow on a multi-layer network, computes noise exposure via a link-to-community noise matrix, and accounts for higher-altitude energy costs, solved through auxiliary-variable linearizations and the convex-concave procedure. A detailed Austin case study demonstrates the approach, revealing actionable design trade-offs and highlighting how ambient masking and fairness considerations can shape feasible, socially sustainable UAM deployments. Overall, the paper delivers a scalable optimization methodology, principled fairness criteria, and practical insights for city-scale UAM planning and policy-making.

Abstract

Urban air mobility (UAM), a transformative concept for the transport of passengers and cargo, faces several integration challenges in complex urban environments. Community acceptance of aircraft noise is among the most noticeable of these challenges when launching or scaling up a UAM system. Properly managing community noise is fundamental to establishing a UAM system that is environmentally and socially sustainable. In this work, we develop a holistic and equitable approach to manage UAM air traffic and its community noise impact in urban environments. The proposed approach is a hybrid approach that considers a mix of different noise mitigation strategies, including limiting the number of operations, cruising at higher altitudes, and ambient noise masking. We tackle the problem through the lens of network system control and formulate a multi-objective optimization model for managing traffic flow in a multi-layer UAM network while concurrently pursuing demand fulfillment, noise control, and energy saving. Further, we use a social welfare function in the optimization model as the basis for the efficiency-fairness trade-off in both demand fulfillment and noise control. We apply the proposed approach to a comprehensive case study in the city of Austin and perform design trade-offs through both visual and quantitative analyses.

Figures (15)

• Figure 1: Single event aircraft noise: sound exposure level (SEL), maximum noise level (Lmax), and duration
• Figure 2: The vehicle configuration considered in this study and noise simulation methods (sources: rizzi2022predictionrizzi2023modeling)
• Figure 3: Simulated NPD data for the NASA RVLT quadrotor vehicle, under normal (left) and log (right) distance scales
• Figure 4: Goodness-of-fit for NPD curves
• Figure 5: Partition and aspects of the 292 communities in the city of Austin