Spatiotemporal Assessment of Aircraft Noise Exposure Using Mobile Phone-Derived Population Estimates and High-Resolution Noise Measurements

2025-04-22

AI Summary

Traditional aviation noise exposure assessment relies on static population data and long-term average noise levels, overlooking dynamic human mobility and time-varying airport operations. This paper proposes a data-driven framework that integrates high-resolution, empirically measured aircraft noise with hourly dynamic population distributions inferred from mobile phone signaling data, enabling fine-grained spatiotemporal exposure quantification. It introduces the “de facto population” concept into aviation noise assessment for the first time, uncovering exposure inequities arising from spatiotemporal mismatches between operational patterns and population presence. The Gini coefficient is adopted to quantify exposure fairness. Case studies demonstrate that runway alternation induces periodic spatial migration of noise exposure, while identical noise events yield exposure counts differing by several-fold due to variations in population presence timing. These findings establish a novel paradigm for precisely identifying vulnerable populations, optimizing noise mitigation strategies, and advancing environmental justice.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Application Domains: Mobility, Driving & Flight

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Security and Privacy: Large-scale security measurements

Abstract

Aircraft noise exposure has traditionally been assessed using static residential population data and long-term average noise metrics, often overlooking the dynamic nature of human mobility and temporal variations in operational conditions. This study proposes a data-driven framework that integrates high-resolution noise measurements from airport monitoring terminals with mobile phone-derived de facto population estimates to evaluate noise exposure with fine spatio-temporal resolution. We develop hourly noise exposure profiles and quantify the number of individuals affected across regions and time windows, using both absolute counts and inequality metrics such as Gini coefficients. This enables a nuanced examination of not only who is exposed, but when and where the burden is concentrated. At our case study airport, operational runway patterns resulted in recurring spatial shifts in noise exposure. By incorporating de facto population data, we demonstrate that identical noise operations can yield unequal impacts depending on the time and location of population presence, highlighting the importance of accounting for population dynamics in exposure assessment. Our approach offers a scalable basis for designing population-sensitive noise abatement strategies, contributing to more equitable and transparent aviation noise management.

Problem

• Assessing aircraft noise exposure with dynamic population data
• Integrating high-resolution noise and mobile-derived population estimates
• Evaluating unequal noise impacts across time and location

Innovation

• Integrates mobile phone data for population estimates
• Uses high-resolution noise measurements from airports
• Develops hourly noise exposure profiles dynamically