Unfolding Road-Related Aspects of Modern Infrastructure in the Future Era of Road Transport

Special Issue Editors


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Guest Editor
Department of Civil Engineering, Democritus University of Thrace (D.U.Th.), Xanthi, Greece
Interests: road-pavement engineering; pavement materials and structures; road asset condition assessment; emerging technologies and nondestructive testing; pavement performance prediction; road safety evaluation; smart road systems
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Civil Engineering, Democritus University of Thrace (D.U.Th.), 67100 Xanthi, Greece
Interests: road geometric design; road safety assessment and human factors; driving simulators; road infrastructure design and management; road functionality; pavements
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The transportation sector is undergoing transformative changes driven by the integration of autonomous and electric vehicles into conventional vehicle fleets. These advancements aim to enhance road safety and promote environmental sustainability. At the same time, physical road infrastructure is increasingly challenged by climatic events, evolving land use patterns, and urban development pressures. In light of these dynamics, it is essential to systematically advance the future readiness of road infrastructure through comprehensive, multidisciplinary research that includes innovations in road design, pavement and material engineering, the integration of nondestructive testing and smart technologies for real-time assessment, and environmentally responsive solutions for road infrastructures. This Special Issue invites original research articles and comprehensive reviews that explore a broad spectrum of topics, including, but not limited to:

  • Infrastructure adaptation to support autonomous vehicles, including upgrades to road design, markings, physical environment, and urban land use.
  • Development of smart roads, vehicle-to-infrastructure (V2I) systems, real-time sensor integration, and advancements in nondestructive evaluation techniques.
  • Sustainable pavement materials with low carbon footprints and their incorporation into pavement design and evaluation frameworks.
  • Environmental considerations such as climate-resilient pavements, mitigation of the urban heat island effect, noise-reducing road surfaces.
  • Societal considerations, equitable access to well-maintained road infrastructure across urban and rural areas, and design and maintenance of pavements for non-motorized users.

We are looking forward to receiving your contributions, to shape the future of road infrastructure for smarter and more resilient road transportation.

Dr. Konstantinos Gkyrtis
Prof. Dr. Alexandros Kokkalis
Guest Editors

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Keywords

  • physical road environment
  • pavement materials and structures
  • environmental aspects of road pavements
  • smart systems and nondestructive testing for roadways
  • future road design and infrastructure resiliency

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Published Papers (1 paper)

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Research

26 pages, 4687 KiB  
Article
Comparative Evaluation of YOLO and Gemini AI Models for Road Damage Detection and Mapping
by Zeynep Demirel, Shvan Tahir Nasraldeen, Öykü Pehlivan, Sarmad Shoman, Mustafa Albdairi and Ali Almusawi
Future Transp. 2025, 5(3), 91; https://doi.org/10.3390/futuretransp5030091 - 22 Jul 2025
Cited by 1 | Viewed by 578
Abstract
Efficient detection of road surface defects is vital for timely maintenance and traffic safety. This study introduces a novel AI-powered web framework, TriRoad AI, that integrates multiple versions of the You Only Look Once (YOLO) object detection algorithms—specifically YOLOv8 and YOLOv11—for automated detection [...] Read more.
Efficient detection of road surface defects is vital for timely maintenance and traffic safety. This study introduces a novel AI-powered web framework, TriRoad AI, that integrates multiple versions of the You Only Look Once (YOLO) object detection algorithms—specifically YOLOv8 and YOLOv11—for automated detection of potholes and cracks. A user-friendly browser interface was developed to enable real-time image analysis, confidence-based prediction filtering, and severity-based geolocation mapping using OpenStreetMap. Experimental evaluation was conducted using two datasets: one from online sources and another from field-collected images in Ankara, Turkey. YOLOv8 achieved a mean accuracy of 88.43% on internet-sourced images, while YOLOv11-B demonstrated higher robustness in challenging field environments with a detection accuracy of 46.15%, and YOLOv8 followed closely with 44.92% on mixed field images. The Gemini AI model, although highly effective in controlled environments (97.64% detection accuracy), exhibited a significant performance drop of up to 80% in complex field scenarios, with its accuracy falling to 18.50%. The proposed platform’s uniqueness lies in its fully integrated, browser-based design, requiring no device-specific installation, and its incorporation of severity classification with interactive geospatial visualization. These contributions address current gaps in generalization, accessibility, and practical deployment, offering a scalable solution for smart infrastructure monitoring and preventive maintenance planning in urban environments. Full article
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