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

A special issue of Future Transportation (ISSN 2673-7590).

Deadline for manuscript submissions: 29 May 2026 | Viewed by 8622

Special Issue Editors

Dr. Konstantinos Gkyrtis

E-Mail Website
Guest Editor
Department of Civil Engineering, Democritus University of Thrace (D.U.Th.), GR-67100 Xanthi, Greece
Interests: pavement condition assessment; non-destructive evaluation; deflectometric testing; smart monitoring systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Alexandros Kokkalis

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

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 250 words) can be sent to the Editorial Office for assessment.

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. Future Transportation is an international peer-reviewed open access semimonthly 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 1200 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

  • 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 (5 papers)

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23 pages, 3858 KB  
Article
Traffic Simulation Analysis Method for Mixed Flow of Intelligent Assisted Driving and Conventional Driving on Class I Highways
by Jiahui Ren, Yingfei Dong, Can Cui, Haining Li and Pengfei Zheng
Future Transp. 2026, 6(2), 53; https://doi.org/10.3390/futuretransp6020053 - 27 Feb 2026
Viewed by 318
Abstract
With the increasing proportion of intelligent assisted vehicles in traffic flow, the existing primary highway traffic management measures exhibit insufficient adaptability to mixed traffic flows with high penetration of such vehicles. This study proposes a simulation analysis method based on SUMO for the [...] Read more.
With the increasing proportion of intelligent assisted vehicles in traffic flow, the existing primary highway traffic management measures exhibit insufficient adaptability to mixed traffic flows with high penetration of such vehicles. This study proposes a simulation analysis method based on SUMO for the primary highway traffic involving mixed flows of vehicles and conventional human-driven vehicles. It elaborates on the simulation configuration, network construction, demand generation, data output and visualization, and selection strategies. A Python-based post-processing tool for simulation results was developed. Gradient control simulation experiments (5% coarse adjustment → 1% fine analysis) were designed to investigate the impact of Connected and Automated Vehicle (CAV) penetration rates and the configuration of a dedicated CAV lane on the inner side of a bidirectional four-lane primary highway on the network Level of Service (LOS). Results indicate that when the CAV penetration rate ranges between 18% and 52%, setting one dedicated lane on the inner side can improve the LOS. However, if the penetration rate is below 18%, such a lane configuration reduces the LOS. When the penetration rate exceeds 52%, the impact becomes negligible. This study establishes a simulation framework for analyzing mixed CAV/conventional vehicle flows on the primary highways, systematically quantifying the penetration rate threshold (18–52%) for CAV-dedicated lanes. This provides a strategic basis for phased implementation based on actual CAV penetration rates and offers a strategic basis for the phased implementation of dedicated CAV lanes on inner lanes of four-lane highways, depending on the actual CAV penetration rate. Full article
(This article belongs to the Special Issue Unfolding Road-Related Aspects of Modern Infrastructure in the Future Era of Road Transport)
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19 pages, 5499 KB  
Article
Smart Crosswalks for Advancing Road Safety in Urban Roads: Conceptualization and Evidence-Based Insights from Greek Incident Records
by Maria Pomoni
Future Transp. 2025, 5(4), 180; https://doi.org/10.3390/futuretransp5040180 - 1 Dec 2025
Cited by 1 | Viewed by 1857
Abstract
Urban intersections are critical for pedestrian safety, as they usually account for high rates of traffic-related injury and fatalities. This study assesses smart crosswalks as an alternative approach to improve road safety that is inherently aligned with the development of intelligent transportation system [...] Read more.
Urban intersections are critical for pedestrian safety, as they usually account for high rates of traffic-related injury and fatalities. This study assesses smart crosswalks as an alternative approach to improve road safety that is inherently aligned with the development of intelligent transportation system technology. After a brief background on this technological advance, this study proceeds with the analysis of long-term crash records from Greek urban roads, concentrating on pedestrians’ behavior in incidents involving road crossing. Thereafter, challenges related to the adoption of an implementation framework are identified. The results confirmed the vulnerability of pedestrians, especially during cases with no specific crossing areas, based on a considerable number of available recorded crashes from a publicly available Greek database. Substantial reductions over the analysis period (i.e., years 2005–2022) in pedestrian-based incidents with injuries and fatalities at a rate of 44% and 52%, respectively, provide evidence-based insights that infrastructural interventions like improved crosswalk design can be translated into measurable benefits for pedestrian safety. Key factors toward a wider applicability framework for even safer interventions through smart crosswalks include maintenance strategies, user education, and systematic integration of funding into urban mobility plans. Full article
(This article belongs to the Special Issue Unfolding Road-Related Aspects of Modern Infrastructure in the Future Era of Road Transport)
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27 pages, 5570 KB  
Article
Floating Car Data for Road Roughness: An Innovative Approach to Optimize Road Surface Monitoring and Maintenance
by Camilla Mazzi, Costanza Carini, Monica Meocci, Andrea Paliotto and Alessandro Marradi
Future Transp. 2025, 5(4), 162; https://doi.org/10.3390/futuretransp5040162 - 3 Nov 2025
Cited by 1 | Viewed by 1197
Abstract
This study investigates the potential of Floating Car Data (FCD) collected from Volkswagen Group vehicles since 2022 for monitoring pavement conditions along two Italian road stretches. While such data are primarily gathered to analyze vehicle dynamics and mechanical behaviour, here, they are repurposed [...] Read more.
This study investigates the potential of Floating Car Data (FCD) collected from Volkswagen Group vehicles since 2022 for monitoring pavement conditions along two Italian road stretches. While such data are primarily gathered to analyze vehicle dynamics and mechanical behaviour, here, they are repurposed to support road network assessment through the estimation of the International Roughness Index (IRI). Daily aggregated datasets provided by NIRA Dynamics were analyzed to evaluate their reliability in detecting spatial and temporal variations in surface conditions. The results show that FCD can effectively identify critical sections requiring maintenance, track IRI variations over time, and assess the performance of surface rehabilitation, with high consistency on single-lane roads. On multi-lane roads, limitations emerged due to data aggregation across lanes, leading to reduced accuracy. Nevertheless, FCD proved to be a cost-efficient and continuously available source of information, particularly valuable for identifying temporal changes and supporting the evaluation of maintenance interventions. Further calibration is needed to enhance alignment with high-performance measurement systems, considering data density at the section level. Overall, the findings highlight the suitability of FCD as a scalable solution for real-time monitoring and long-term maintenance planning, contributing to more sustainable management of road infrastructure. Full article
(This article belongs to the Special Issue Unfolding Road-Related Aspects of Modern Infrastructure in the Future Era of Road Transport)
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26 pages, 4687 KB  
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 4 | Viewed by 3650
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
(This article belongs to the Special Issue Unfolding Road-Related Aspects of Modern Infrastructure in the Future Era of Road Transport)
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22 pages, 1366 KB  
Systematic Review
Inspection and Evaluation of Urban Pavement Deterioration Using Drones: Review of Methods, Challenges, and Future Trends
by Pablo Julián López-González, David Reyes-González, Oscar Moreno-Vázquez, Rodrigo Vivar-Ocampo, Sergio Aurelio Zamora-Castro, Lorena del Carmen Santos Cortés, Brenda Suemy Trujillo-García and Joaquín Sangabriel-Lomelí
Future Transp. 2026, 6(1), 10; https://doi.org/10.3390/futuretransp6010010 - 4 Jan 2026
Viewed by 926
Abstract
The rapid growth of urban areas has increased the need for more efficient methods of pavement inspection and maintenance. However, conventional techniques remain slow, labor-intensive, and limited in spatial coverage, and their performance is strongly affected by traffic, weather conditions, and operational constraints. [...] Read more.
The rapid growth of urban areas has increased the need for more efficient methods of pavement inspection and maintenance. However, conventional techniques remain slow, labor-intensive, and limited in spatial coverage, and their performance is strongly affected by traffic, weather conditions, and operational constraints. In response to these challenges, it is essential to synthesize the technological advances that improve inspection efficiency, coverage, and data quality compared to traditional approaches. Herein, we present a systematic review of the state of the art on the use of unmanned aerial vehicles (UAVs) for monitoring and assessing pavement deterioration, highlighting as a key contribution the comparative integration of sensors (photogrammetry, LiDAR, and thermography) with recent automatic damage-detection algorithms. A structured review methodology was applied, including the search, selection, and critical analysis of specialized studies on UAV-based pavement evaluation. The results indicate that UAV photogrammetry can achieve sub-centimeter accuracy (<1 cm) in 3D reconstructions, LiDAR systems can improve deformation detection by up to 35%, and AI-based algorithms can increase crack-identification accuracy by 10% to 25% compared with manual methods. Finally, the synthesis shows that multi-sensor integration and digital twins offer strong potential to enhance predictive maintenance and support the transition towards smarter and more sustainable urban infrastructure management strategies. Full article
(This article belongs to the Special Issue Unfolding Road-Related Aspects of Modern Infrastructure in the Future Era of Road Transport)
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