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Sustainable Transportation and Infrastructure Management

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 856

Editors


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Guest Editor
Department of Engineering, Pegaso Telematic University, 80143 Napoli, Italy
Interests: roads; railways; airport infrastructure
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Engineering, Pegaso Telematic University, 80143 Napoli, Italy
Interests: science of cities; territorial complexities; urban governance
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Guest Editor
Department of Engineering, University of Palermo, 90128 Palermo, Italy
Interests: pavement engineering; advanced machine learning; railways; airports
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, “Sustainable Transportation and Infrastructure Management”, addresses the integrated planning, operation, and preservation of transport infrastructure and systems in line with the evolving principles of environmental, economic, and social sustainability.

The Special Issue will focus on innovative management strategies, advanced materials, and digital methodologies that enhance the efficiency, resilience, and safety of multimodal transport networks.

Its scope includes infrastructure asset management, sustainable construction and maintenance techniques, performance monitoring, predictive modeling, and the use of smart technologies and data-driven approaches to optimize life-cycle performance and resource efficiency.

The purpose of this Special Issue is to collect high-quality contributions that demonstrate how sustainability-oriented management can transform transportation systems and infrastructure design practices, bridging the gap between policy frameworks, engineering methods, and operational outcomes.

By connecting scientific research with international sustainability goals, such as the United Nations Sustainable Development Goals and the European Green Deal, this Special Issue aims to supplement the existing literature with cross-disciplinary perspectives and practical case studies that highlight the transition towards resilient, low-impact, and intelligent infrastructure systems, as well as through the integration of sustainable regeneration processes and geoAI.

Dr. Francesco Abbondati
Dr. Ferdinando Verardi
Dr. Fabio Rondinella
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability 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 2400 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

  • sustainable transportation management
  • infrastructure asset management
  • life-cycle performance
  • smart mobility systems
  • resilient transport networks
  • sustainable construction and maintenance
  • data-driven infrastructure optimization
  • predictive modeling in pavement engineering
  • environmental and social sustainability
  • digital twin of infrastructures
  • GeoAI

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Published Papers (1 paper)

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Research

20 pages, 2249 KB  
Article
Pavement Roughness as a Multiscale Spatial Process: Insight from Crowdsensed Data
by Francesco Abbondati, Ferdinando Verardi, Antonio Setaro and Cristina Oreto
Sustainability 2026, 18(12), 5796; https://doi.org/10.3390/su18125796 - 6 Jun 2026
Viewed by 355
Abstract
Magnitude alone fails to capture the full complexity of pavement roughness; its spatial distribution along a road is equally vital for effective maintenance planning. While traditional assessment has long relied on specialized survey vehicles, the rise of mobile crowdsensing now allows for massive [...] Read more.
Magnitude alone fails to capture the full complexity of pavement roughness; its spatial distribution along a road is equally vital for effective maintenance planning. While traditional assessment has long relied on specialized survey vehicles, the rise of mobile crowdsensing now allows for massive data acquisition via smartphone sensors. This study investigates the spatial structure of pavement roughness using crowdsensed data from the SmartRoadSense platform. Roughness is quantified through the Power of Prediction Error (PPE) indicator derived from smartphone accelerometer signals. The dataset consists of 475 observations sampled at 20 m intervals over approximately 9.5 km of the A3/E45 motorway in southern Italy. A multi-scale spatial–statistical framework is adopted to analyse the roughness signal. The analysis includes the evaluation of scale-dependent statistical descriptors (mean and coefficient of variation), as well as spatial correlation, spectral, and entropy-based measures. The results indicate a short spatial correlation length (approximately 60–100 m) and the absence of a dominant spatial wavelength, suggesting that pavement roughness behaves as a localized multiscale process. A complementary segmentation analysis based on Classification and Regression Trees (CART) is performed to explore the spatial partitioning of the roughness signal. Our analysis indicates that segmentation complexity spikes once the minimum node size drops below roughly 10 observations. This trend points to the existence of localized irregularities that coarser scales simply overlook. Ultimately, these results suggest that mean roughness values alone are insufficient for describing pavement condition and that hybrid spatial–statistical approaches may support more scalable, data-driven, and spatially targeted pavement monitoring strategies for sustainable transportation infrastructure management. Full article
(This article belongs to the Special Issue Sustainable Transportation and Infrastructure Management)
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