| Abstract |
Highlights: What are the main findings? Drone deliveries under windy conditions are viable and lead to improved efficiency and reduced air and noise pollution compared to traditional delivery methods. Noise pollution requires further study to incorporate dynamic, spatiotemporal-oriented factors, including meteorological conditions, to better mitigate its impact on urban environments. What are the implications of the main findings? Integrating drones in last-mile delivery can help cities meet zero pollution goals, particularly for reducing air pollution. Integrated approaches addressing both environmental and social factors, particularly noise pollution, are essential for sustainable drone delivery ecosystems. As cities expand and the global push for zero pollution intensifies, sustainable last-mile delivery (LMD) systems are essential to minimizing environmental and health impacts. This study addresses the need for more sustainable LMD by examining the integration of wind conditions into drone-assisted deliveries, focusing on their effects on air and noise pollution in urban areas. We extend the flying sidekick traveling salesman problem (FSTSP) by incorporating meteorological factors, specifically wind, to assess drone delivery efficiency in varying conditions. Our results show that while drones significantly reduce greenhouse gas emissions compared to traditional delivery vehicles, their contribution to noise pollution remains a concern. This research highlights the environmental advantages of using drones, particularly in reducing CO2 emissions, while also emphasizing the need for further investigation into mitigating their noise impact. By evaluating the trade-offs between air and noise pollution, this study provides insights into developing more sustainable, health-conscious delivery models that contribute to smart city initiatives. The findings inform policy, urban planning, and logistics strategies aimed at achieving zero pollution goals and improving urban livability. © 2024 by the authors. |
