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Drones
Special Issues
Urban Traffic Monitoring and Analysis Using UAVs
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Urban Traffic Monitoring and Analysis Using UAVs

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

Deadline for manuscript submissions: 15 July 2025 | Viewed by 1163

Special Issue Editors

Dr. Wim Ectors

E-Mail Website
Guest Editor
Faculty of Science of the Department of Mobility, Universiteit Hasselt, Hasselt, Belgium
Interests: intelligent transport systems; infrastructure, transport and mobility engineering; transportation planning; environmental and sustainable planning; smart cities; traffic engineering; movement behavior; mobility; road safety
Prof. Dr. Ansar Yasar

E-Mail Website
Guest Editor
Transportation Research Institute (IMOB), Hasselt University, 3500 Hasselt, Belgium
Interests: ITS; vehicular networks; agent-based modeling; modeling and simulation; transportation behavior
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Stéphane Galland

E-Mail Website
Guest Editor
Laboratoire Connaissance et Intelligence Artificielle Distribuées (CIAD), Université de Technologie de Belfort-Montbéliard, 90010 Belfort, France
Interests: multiagent systems; agent-based simulation; Janus multi-agent platform; ASPECS agent-based methodology; Holonic systems; virtual life simulation; 3D and virtual reality; multilevel simulation; urban simulation; transport system simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Urban traffic management has become increasingly complex due to the rapid growth of urban populations and the corresponding rise in vehicular traffic. Traditional methods of traffic monitoring often fall short in providing real-time data and insights necessary for effective decision-making or law enforcement. The integration of Unmanned Aerial Vehicles (UAVs) into traffic monitoring systems offers innovative solutions to these challenges. UAVs can cover large areas quickly, provide high-resolution imagery, and utilize advanced data analytics to enhance traffic management and enforcement strategies.

This special issue aims to compile cutting-edge research on the application of UAVs in urban traffic monitoring and analysis. It seeks to explore innovative methodologies, applications, and technologies that leverage UAV capabilities to address urban traffic challenges (traffic flow, enhance safety, smarter urban planning and enforcement initiatives etc.).

We invite original research articles, comprehensive reviews, and insightful case studies on urban traffic monitoring topics including, but not limited to:

  • Innovative UAV-based traffic (safety) data acquisition and processing techniques
  • Real-time traffic monitoring and event detection using UAVs
  • Integration of UAV data with traffic management systems
  • AI and machine learning applications in UAV traffic analysis
  • Multi-UAV coordination for comprehensive urban traffic surveillance
  • Legal, ethical, policy and safety considerations in UAV deployment over urban areas
  • Case studies of UAV implementation in urban traffic scenarios such as monitoring, enforcement, analysis etc.
  • Environmental impacts of UAV-based traffic monitoring

Types of articles encouraged include original research papers, review articles, and case studies.

Dr. Wim Ectors
Prof. Dr. Ansar Yasar
Prof. Dr. Stéphane Galland
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

  • urban traffic monitoring
  • unmanned aerial vehicles (UAVs)
  • data analytics
  • smart cities
  • traffic flow optimization
  • traffic safety
  • real-time
  • intelligent transportation systems
  • multi-UAV systems
  • traffic enforcement

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

Published Papers (2 papers)

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Research

27 pages, 7288 KiB  
Article
Digital Low-Altitude Airspace Unmanned Aerial Vehicle Path Planning and Operational Capacity Assessment in Urban Risk Environments
by Ouge Feng, Honghai Zhang, Weibin Tang, Fei Wang, Dikun Feng and Gang Zhong
Drones 2025, 9(5), 320; https://doi.org/10.3390/drones9050320 - 22 Apr 2025
Abstract
This paper proposes a digital low-altitude airspace unmanned aerial vehicle (UAV) path planning method tailored for urban risk environments and conducts an operational capacity assessment of the airspace. The study employs a vertical–horizontal grid partitioning technique to achieve airspace grid-based modeling, classifying and [...] Read more.
This paper proposes a digital low-altitude airspace unmanned aerial vehicle (UAV) path planning method tailored for urban risk environments and conducts an operational capacity assessment of the airspace. The study employs a vertical–horizontal grid partitioning technique to achieve airspace grid-based modeling, classifying and configuring “management-operation” grids. By integrating multi-source heterogeneous data, including building structures, population density, and sheltering factor, a grid-based discrete risk quantification model is established to evaluate comprehensive mid-air collision risk, ground impact risk, third-party risk, and UAV turning risk. A path planning method considering the optimization of the turning points of parallelograms was proposed, and the Parallel-A* algorithm was adopted for its solution. Finally, an airspace operational capacity assessment model and a conflict simulation model for urban risk environments are developed to quantify the operational capacity of urban low-altitude airspace. Using Liuhe District in Nanjing as the experimental area, the study reveals that the environmental airspace risk decreases significantly with increasing flight altitude and eventually stabilizes. In the implementation of path planning, compared with the A* and Weight-A* algorithms, the Parallel-A* algorithm demonstrates clear advantages in terms of lower average comprehensive risk and fewer turning points. In the operational capacity assessment experiments, the airspace capacity across different altitude layers increases with flight altitude and stabilizes after comprehensive risk is reduced. This research provides a theoretical foundation for the scientific management and optimal resource allocation of urban low-altitude airspace, facilitating the safe application and sustainable development of UAVs in urban environments. Full article
(This article belongs to the Special Issue Urban Traffic Monitoring and Analysis Using UAVs)
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20 pages, 13525 KiB  
Article
Fixed/Mobile Collaborative Traffic Flow Detection Study Based on Wireless Charging of UAVs
by Hao Wu, Mingbo Niu, Biao Wang, Kai Yan, Yuxuan Li and Hanyu Pang
Drones 2025, 9(2), 117; https://doi.org/10.3390/drones9020117 - 5 Feb 2025
Viewed by 577
Abstract
Accurate traffic flow detection plays a critical role in intelligent traffic control systems. However, conventional fixed video detection devices often face challenges such as occlusion and overlap in high-density traffic scenarios, which leads to distortions in vehicle detection. To address this issue, it [...] Read more.
Accurate traffic flow detection plays a critical role in intelligent traffic control systems. However, conventional fixed video detection devices often face challenges such as occlusion and overlap in high-density traffic scenarios, which leads to distortions in vehicle detection. To address this issue, it is essential to obtain precise vehicle data as a reliable reference for managing traffic flow during peak periods. In this paper, we propose an intelligent detection scheme using an improved YOLOv8n target recognition algorithm combined with a ByteTrack multi-target tracking algorithm. A collaborative unmanned aerial vehicle (UAV) collaborative detection framework is also established, integrating UAVs and fixed detection devices to work in tandem. Such a multi-UAV collaborative data acquiring system is designed for efficient, continuous, and uninterrupted operation, employing a three-drone rotational detection strategy. UAVs offer additional flexibility and coverage in obtaining vehicle data. However, limited power could be an essential challenge to the system’s wireless physical link stability and safety. To overcome power limitations during UAV collaboration, a wireless charging (WC) system is introduced, enabling automatic constant current–constant voltage (CC-CV) switching and preventing damage from accidental data link disabling. This collaborative traffic data acquiring and transmission system ensures a stable power supply for UAVs during high-density traffic periods, supporting their reliable UAV collaborative wireless data link. Experimental results show that the collaborative detection architecture combined with wireless charging can achieve high detection accuracy, with the recognition accuracy remaining between 0.95 and 0.99. Full article
(This article belongs to the Special Issue Urban Traffic Monitoring and Analysis Using UAVs)
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