Special Issue "Transportation Safety and Pavement Management"

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

Deadline for manuscript submissions: 30 April 2022.

Special Issue Editors

Prof. Dr. Maria Rosaria De Blasiis
E-Mail Website
Guest Editor
Department of Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy
Interests: construction and infrastructure systems engineering; road design; pavement design and maiteinance; road safety; environmetal analysis
Dr. Chiara Ferrante
E-Mail Website
Guest Editor
Department of Engineering, Roma Tre University, Via Vito Volterra 60, 00146 Rome, Italy
Interests: road design; maintenance and rehabilitation; road safety
Dr. Valerio Veraldi
E-Mail Website
Guest Editor
R.I.S.E. Srl - Research and Innovation for Sustainable Environment, Via Giacomo Trevis 88, 00147 Roma, Italy
Interests: road design; sustanible maintenance; road safety; environmetal analysis

Special Issue Information

Dear Colleagues,

A decisive contribution to the sustainability of an infrastructure is provided by a systemic approach of infrastructure management and safety; through effective treatments, the occurrence of accidents in high-risk infrastructures can be reduced.

The introduction of new survey techniques able to reconstruct the whole geometric surface together with complex simulation processes allow a reliable reproduction of driving conditions, therefore allowing the evaluation of comfort, risky conditions, and hazardous maneuvers, which often lead to accidents.

The evolution of new technologies (construction and survey methodologies) is giving a remarkable contribution to the reduction of accidents. In fact, recent advances in simulation technologies in computational optimization and building technologies are leading toward an improvement in quality and in infrastructure safety levels (ITS, ADAS, etc.).

In the present Special Issue, we welcome research that enhances stakeholders’ ability to develop strategic and systemic decisions regarding the design, construction, maintenance, and operation of transport systems.

Therefore, the Special Issue is focused on the use of innovative technologies that improve the safety and sustainability of transportation infrastructures, both from users and designers.

Furthermore, new methodologies and enhanced studies concerning geometric design, surface pavement characteristics, paving materials and their mix design, and environmental and weather conditions are necessary to improve the evaluation of road functionality and safety.

Therefore, in summary, this Special Issue welcomes but is not limited to original research and reviews on the following topics:

  • Safety assessment methodologies;
  • Optimization models for accident analysis;
  • Innovative technologies for survey and evaluation of pavement surface characteristics;
  • Pavement management system;
  • Performance indexed for pavement;
  • Modeling deterioration of pavement;
  • Environment;
  • Intelligent transport systems;
  • Advanced driver-assistance systems;
  • Smart transportation infrastructures.

Prof. Dr. Maria Rosaria De Blasiis
Dr. Chiara Ferrante
Dr. Valerio Veraldi
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 papers will be 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. 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 1900 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

  • transportation safety
  • pavement management
  • maintenance and rehabilitation
  • simulation models
  • survey innovative technologies
  • surface characteristics
  • pavement monitoring
  • risk perception
  • accident analysis
  • human behavior

Published Papers (5 papers)

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Research

Article
Surrogate Safety Measures Prediction at Multiple Timescales in V2P Conflicts Based on Gated Recurrent Unit
Sustainability 2021, 13(17), 9681; https://doi.org/10.3390/su13179681 (registering DOI) - 28 Aug 2021
Viewed by 180
Abstract
Improving pedestrian safety at urban intersections requires intelligent systems that should not only understand the actual vehicle–pedestrian (V2P) interaction state but also proactively anticipate the event’s future severity pattern. This paper presents a Gated Recurrent Unit-based system that aims to predict, up to [...] Read more.
Improving pedestrian safety at urban intersections requires intelligent systems that should not only understand the actual vehicle–pedestrian (V2P) interaction state but also proactively anticipate the event’s future severity pattern. This paper presents a Gated Recurrent Unit-based system that aims to predict, up to 3 s ahead in time, the severity level of V2P encounters, depending on the current scene representation drawn from on-board radars’ data. A car-driving simulator experiment has been designed to collect sequential mobility features on a cohort of 65 licensed university students who faced different V2P conflicts on a planned urban route. To accurately describe the pedestrian safety condition during the encounter process, a combination of surrogate safety indicators, namely TAdv (Time Advantage) and T2 (Nearness of the Encroachment), are considered for modeling. Due to the nature of these indicators, multiple recurrent neural networks are trained to separately predict T2 continuous values and TAdv categories. Afterwards, their predictions are exploited to label serious conflict interactions. As a comparison, an additional Gated Recurrent Unit (GRU) neural network is developed to directly predict the severity level of inner-city encounters. The latter neural model reaches the best performance on the test set, scoring a recall value of 0.899. Based on selected threshold values, the presented models can be used to label pedestrians near accident events and to enhance existing intelligent driving systems. Full article
(This article belongs to the Special Issue Transportation Safety and Pavement Management)
Article
Proposal and Implementation of a Heliport Pavement Management System: Technical and Economic Comparison of Maintenance Strategies
Sustainability 2021, 13(16), 9201; https://doi.org/10.3390/su13169201 - 17 Aug 2021
Viewed by 331
Abstract
Maintenance and rehabilitation (M&R) scheduling for airport pavement is supported by the scientific literature, while a specific tool for heliport pavements lacks. A heliport pavement management system (HPMS) allows the infrastructure manager to obtain benefits in technical and economic terms, as well as [...] Read more.
Maintenance and rehabilitation (M&R) scheduling for airport pavement is supported by the scientific literature, while a specific tool for heliport pavements lacks. A heliport pavement management system (HPMS) allows the infrastructure manager to obtain benefits in technical and economic terms, as well as safety and efficiency, during the analyzed period. Structure and rationale of the APSM could be replicated and simplified to implement a HPMS because movements of rotary-wing aircrafts have less complexity than fixed-wing ones and have lower mechanical effects on the pavement. In this study, an innovative pavement condition index-based HPMS has been proposed and implemented to rigid and flexible surfaces of the airport of Vergiate (province of Varese, Italy), and two twenty-year M&R plans have been developed, where the results from reactive and proactive approaches have been compared to identify the best strategy in terms of costs and pavement level of service. The result obtained shows that although the loads and traffic of rotary-wing aircrafts are limited, the adoption of PMS is also necessary in the heliport environment. Full article
(This article belongs to the Special Issue Transportation Safety and Pavement Management)
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Article
A Machine Learning Approach to Determine Airport Asphalt Concrete Layer Moduli Using Heavy Weight Deflectometer Data
Sustainability 2021, 13(16), 8831; https://doi.org/10.3390/su13168831 - 06 Aug 2021
Viewed by 362
Abstract
An integrated approach based on machine learning and data augmentation techniques has been developed in order to predict the stiffness modulus of the asphalt concrete layer of an airport runway, from data acquired with a heavy weight deflectometer (HWD). The predictive model relies [...] Read more.
An integrated approach based on machine learning and data augmentation techniques has been developed in order to predict the stiffness modulus of the asphalt concrete layer of an airport runway, from data acquired with a heavy weight deflectometer (HWD). The predictive model relies on a shallow neural network (SNN) trained with the results of a backcalculation, by means of a data augmentation method and can produce estimations of the stiffness modulus even at runway points not yet sampled. The Bayesian regularization algorithm was used for training of the feedforward backpropagation SNN, and a k-fold cross-validation procedure was implemented for a fair performance evaluation. The testing phase result concerning the stiffness modulus prediction was characterized by a coefficient of correlation equal to 0.9864 demonstrating that the proposed neural approach is fully reliable for performance evaluation of airfield pavements or any other paved area. Such a performance prediction model can play a crucial role in airport pavement management systems (APMS), allowing the maintenance budget to be optimized. Full article
(This article belongs to the Special Issue Transportation Safety and Pavement Management)
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Article
Applying Machine Learning to Develop Lane Control Principles for Mixed Traffic
Sustainability 2021, 13(14), 7656; https://doi.org/10.3390/su13147656 - 08 Jul 2021
Viewed by 434
Abstract
The mixed traffic environment often has high accident rates. Therefore, many motorcycle-related traffic improvements or control methods are employed in countries with mixed traffic, including slow-traffic lanes, motorcycle two-stage left turn areas, and motorcycle waiting zones. In Taiwan, motorcycles can ride in only [...] Read more.
The mixed traffic environment often has high accident rates. Therefore, many motorcycle-related traffic improvements or control methods are employed in countries with mixed traffic, including slow-traffic lanes, motorcycle two-stage left turn areas, and motorcycle waiting zones. In Taiwan, motorcycles can ride in only the two outermost lanes, including the curb lane and a mixed traffic lane. This study analyzed the new motorcycle-riding space control policy on 27 major arterial roads containing 248 road segments in Taipei by analyzing before-and-after accident data from the years 2012–2018. In this study, the equivalent-property-damage-only (EPDO) method was used to evaluate the severity of crashes before and after the cancelation of the third lane prohibition of motorcycles (TLPM) policy. After EPDO analysis, the random forest analysis method was used to screen the crucial factors in accidents for specific road segments. Finally, a classification and regression tree (CART) was created to predict the accident improvement effects of the road segments with discontinued TLPM in different situations. Furthermore, to provide practical applications, this study integrated the CART results and the needs of traffic authorities to determine four rules for canceling TLPM. In the future, on the accident-prone road segment with TLPM, the inspection of the four rules can provide the authority to decide whether to cancel TLPM to improve the accident or not. Full article
(This article belongs to the Special Issue Transportation Safety and Pavement Management)
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Article
Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models
Sustainability 2021, 13(9), 5248; https://doi.org/10.3390/su13095248 - 07 May 2021
Cited by 1 | Viewed by 826
Abstract
Road transportation has always been inherent in developing societies, impacting between 10–20% of Gross Domestic Product (GDP). It is responsible for personal mobility (access to services, goods, and leisure), and that is why world economies rely upon the efficient and safe functioning of [...] Read more.
Road transportation has always been inherent in developing societies, impacting between 10–20% of Gross Domestic Product (GDP). It is responsible for personal mobility (access to services, goods, and leisure), and that is why world economies rely upon the efficient and safe functioning of transportation facilities. Road maintenance is vital since the need for maintenance increases as road infrastructure ages and is based on sustainability, meaning that spending money now saves much more in the future. Furthermore, road maintenance plays a significant role in road safety. However, pavement management is a challenging task because available budgets are limited. Road agencies need to set programming plans for the short term and the long term to select and schedule maintenance and rehabilitation operations. Pavement performance prediction models (PPPMs) are a crucial element in pavement management systems (PMSs), providing the prediction of distresses and, therefore, allowing active and efficient management. This work aims to review the modeling techniques that are commonly used in the development of these models. The pavement deterioration process is stochastic by nature. It requires complex deterministic or probabilistic modeling techniques, which will be presented here, as well as the advantages and disadvantages of each of them. Finally, conclusions will be drawn, and some guidelines to support the development of PPPMs will be proposed. Full article
(This article belongs to the Special Issue Transportation Safety and Pavement Management)
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