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Drones
Special Issues
Urban Air Mobility Solutions: UAVs for Smarter Cities
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Urban Air Mobility Solutions: UAVs for Smarter Cities

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Innovative Urban Mobility".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 627

Special Issue Editors

Dr. Sarah Hubbard

E-Mail Website
Guest Editor
Purdue University School of Aviation and Transportation Technology, Purdue University Polytechnic Institute, West Lafayette, IN 47907, USA
Interests: transportation innovation; UAV applications; safety; sustainability; airport
Dr. Damon Lercel

E-Mail Website
Guest Editor
Purdue University School of Aviation and Transportation Technology, Purdue University Polytechnic Institute, West Lafayette, IN 47907, USA
Interests: autonomous aviation; UTM, safety; aviation maintenance
Dr. Cheng Wang

E-Mail Website
Guest Editor
Department of Aviation, Minnesota State University, Mankato, MN 56001, USA
Interests: drone sighting; UAS integration; aviation safety; runway incursion

Special Issue Information

Dear Colleagues,

Urban Air Mobility (UAM) is a dynamic field that has grown in interest due to advances in aircraft technologies and automation.  This Special Issue will examine the many ways that autonomous aerial vehicles (AAV) can contribute to smarter cities, as well as explore potential solutions to the operational, technical, planning, and safety challenges. UAM may fulfill many functions in the smart city of the future by providing an alternative mode of transportation to congested roadways, enabled by next-generation VTOL technologies powered by clean renewable energy sources, reducing local emissions and noise. These VTOL technologies offer the potential to improve the mobility of people and cargo and may be especially useful for last-mile delivery, emergency response, and critical medical transport. Additionally, AAVs support a diverse number of technological sensor solutions, which enable real-time data collection to improve infrastructure monitoring, maintenance, and transportation efficiencies. Other opportunities may include AAVs as airborne communication nodes that may improve data connectivity, weather forecasting, and natural disaster response. Given the numerous applications and benefits, AAVs will play an important role in the smart cities of the future, redefining current business models, creating new business opportunities, and improving the lives of the public.

This special issue aims to highlight and discuss the critical issues, solutions, and opportunities related to the use of UAM and AAV technologies in the Smart Cities of the future. This aligns closely with the journal's scope of Drones, which focuses on the design and application of drones, including UAS, UAVs, and RPAS, for various use cases. Submissions should focus primarily on drones—whether aerial (such as unmanned aerial vehicles or UAVs) or terrestrial (including unmanned ground vehicles or UGVs)—and their application in urban mobility. Manuscripts that broadly address urban air mobility (UAM) concepts without a significant focus on drones will not align with the section's scope.

  • Economic analysis, opportunities and benefits of UAV and UAM
  • Environmental benefits of UAV and UAM
  • Urban policies to support UAM and UAV in urban areas
  • Examples and operational models of UAM and UAV including emergency services, last-mile delivery, and routine transport of people and cargo
  • Technical papers addressing operational issues, safety issues, infrastructure issues
  • Workforce implications and training needs of UAV and UAM in smart cities

Dr. Sarah Hubbard
Dr. Damon Lercel
Dr. Cheng Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • UAM
  • UAV
  • smart cities
  • infrastructure
  • safety
  • funding
  • public policy
  • urban planning
  • emergency response
  • workforce

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

27 pages, 7066 KiB  
Article
A Deep Learning-Based Trajectory and Collision Prediction Framework for Safe Urban Air Mobility
by Junghoon Kim, Hyewon Yoon, Seungwon Yoon, Yongmin Kwon and Kyuchul Lee
Drones 2025, 9(7), 460; https://doi.org/10.3390/drones9070460 - 26 Jun 2025
Viewed by 423
Abstract
As urban air mobility moves rapidly toward real-world deployment, accurate vehicle trajectory prediction and early collision risk detection are vital for safe low-altitude operations. This study presents a deep learning framework based on an LSTM–Attention network that captures both short-term flight dynamics and [...] Read more.
As urban air mobility moves rapidly toward real-world deployment, accurate vehicle trajectory prediction and early collision risk detection are vital for safe low-altitude operations. This study presents a deep learning framework based on an LSTM–Attention network that captures both short-term flight dynamics and long-range dependencies in trajectory data. The model is trained on fifty-six routes generated from a UAM planned commercialization network, sampled at 0.1 s intervals. To unify spatial dimensions, the model uses Earth-Centered Earth-Fixed (ECEF) coordinates, enabling efficient Euclidean distance calculations. The trajectory prediction component achieves an RMSE of 0.2172, MAE of 0.1668, and MSE of 0.0524. The collision classification module built on the LSTM–Attention prediction backbone delivers an accuracy of 0.9881. Analysis of attention weight distributions reveals which temporal segments most influence model outputs, enhancing interpretability and guiding future refinements. Moreover, this model is embedded within the Short-Term Conflict Alert component of the Safety Nets module in the UAM traffic management system to provide continuous trajectory prediction and collision risk assessment, supporting proactive traffic control. The system exhibits robust generalizability on unseen scenarios and offers a scalable foundation for enhancing operational safety. Validation currently excludes environmental disturbances such as wind, physical obstacles, and real-world flight logs. Future work will incorporate atmospheric variability, sensor and communication uncertainties, and obstacle detection inputs to advance toward a fully integrated traffic management solution with comprehensive situational awareness. Full article
(This article belongs to the Special Issue Urban Air Mobility Solutions: UAVs for Smarter Cities)
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