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Smart Cities: AI-Enhanced Urban Living

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 January 2026) | Viewed by 1752

Special Issue Editors


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Guest Editor
College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China
Interests: construction; construction engineering; structural analysis; building materials; civil engineering materials; construction materials; environment; agriculture; sustainable agriculture; environmental impact assessment

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Guest Editor
Department of Infrastructure Engineering, University of Melbourne, Parkville, VIC 3010, Australia
Interests: sustainable construction materials; fit-for-purpose concrete mix selection; concrete technology and performance; in situ health diagnostics and monitoring of civil infrastructure
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid evolution of technology, particularly in the fields of artificial intelligence (AI) and the Internet of Things (IoT), has transformed how we perceive and interact with urban environments. The concept of a "smart city" embodies this transformation, leveraging advanced technologies to enhance urban living, improve infrastructure efficiency, foster sustainability, and enhance quality of life for residents. As cities worldwide increasingly adopt smart technologies, AI has emerged as a cornerstone in optimizing city operations, improving public services, and fostering sustainable development. The rapid advancement of artificial intelligence (AI) technologies is transforming the way that we live, work, and interact within urban environments. Smart cities, characterized by their integration of digital technology and data-driven decision-making, are at the forefront of this transformation. This Special Issue will explore the multifaceted dimensions of AI-enhanced smart cities, focusing on the latest innovations, challenges, and future directions, as well as the concept’s impact on the quality of life for city dwellers. This Special Issue will publish high-quality, original research papers in the following overlapping fields:

  • AI-assisted urban design and planning;
  • AI in infrastructure and transportation;
  • AI in public safety and security;
  • AI in environmental sustainability;
  • AI in energy and environmental management:
  • AI in citizen engagement and services;
  • AI in urban governance;
  • AI in telemedicine and public health;
  • Successful AI implementations in smart cities;
  • Ethical, legal, and social implications.

Dr. Hongtao Peng
Dr. Massoud Sofi
Guest Editors

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Keywords

  • artificial intelligence (AI)
  • internet of things (IoT)
  • smart city
  • urban development
  • ethics and moral principles
  • urban living

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Published Papers (1 paper)

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Research

39 pages, 10175 KB  
Article
EdgeML-Driven Real-Time Vehicle Tracking and Traffic Control for Traffic Management in Smart Cities
by Hyago V. L. B. Silva, Davi Rosim, Felipe A. P. de Figueiredo, Samuel B. Mafra, Ahmed S. Khwaja and Alagan Anpalagan
Appl. Sci. 2026, 16(5), 2216; https://doi.org/10.3390/app16052216 - 25 Feb 2026
Viewed by 670
Abstract
The escalating global rates of traffic accidents in urban areas and the growing demands of smart cities underscore the urgent need for advanced real-time monitoring solutions. This paper presents an EdgeML-based system for vehicle tracking that performs real-time speed and distance analysis and [...] Read more.
The escalating global rates of traffic accidents in urban areas and the growing demands of smart cities underscore the urgent need for advanced real-time monitoring solutions. This paper presents an EdgeML-based system for vehicle tracking that performs real-time speed and distance analysis and traffic violation detection. This is achieved by deploying a YOLOv8 object detection model on a Raspberry Pi 5 with a Coral USB Edge TPU accelerator. The system integrates computer vision and IoT technologies to enable real-time processing. It utilizes the Message Queuing Telemetry Transport (MQTT) protocol to allow scalable communication between distributed edge devices and a central MongoDB database, facilitating real-time storage and analysis of traffic data. A synthetic dataset generated via the Blender 3D modeling tool validates the system’s accuracy, demonstrating average speed and distance measurement errors of ±2.11 km/h and ±0.58 m, respectively. These findings are further supported by preliminary practical experiments in a real-world environment, where speed estimation errors remained within 0–2 km/h and distance errors stayed below 0.11 m. Key innovations of this work include license plate recognition, speeding and collision detection, and context analysis using Google’s Gemini-2.5-Flash API. A Streamlit dashboard provides real-time visualization of traffic metrics, violations, and aggregated data. A comparative evaluation of YOLOv5n, YOLOv8n, YOLOv11n, and YOLOv12n identifies YOLOv8n as the most suitable model for embedded deployment, achieving 91.07 ± 0.61% mAP@0.5 without quantization, 88.77 ± 3.31% mAP@0.5 with quantization, while maintaining real-time performance of 30–43 frames per second (FPS) on the Edge TPU. The system’s modular architecture, low latency, and robust performance highlight its suitability for smart city applications, enhancing traffic safety and enabling data-driven urban mobility management. Full article
(This article belongs to the Special Issue Smart Cities: AI-Enhanced Urban Living)
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