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

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 32686

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Special Issue Editors

Prof. Dr. Maria Rosaria De Blasiis

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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

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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

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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

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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 (12 papers)

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Research

15 pages, 1384 KiB

Open AccessArticle

Influence of Intersection Density on Risk Perception of Drivers in Rural Roadways: A Driving Simulator Study

by Samyajit Basu, Chiara Ferrante and Maria Rosaria De Blasiis

Sustainability 2022, 14(13), 7750; https://doi.org/10.3390/su14137750 - 25 Jun 2022

Viewed by 1353

Abstract

With the aim of maintaining a decent level of accessibility, the presence of intersections, often in high numbers, is one of the typical features of rural roads. However, evidence from literature shows that increasing intersection density increases the risk of accidents. Accident analysis literature regarding intersection density primarily consists of accident prediction models which are a useful tool for measuring safety performance of roads, but the literature is lacking in terms of evaluation of driver behavior using direct measurements of driver performance. This study focuses on the influence of intersection density on the risk perception of drivers through experiments carried out with a driving simulator. A virtual driving environment of a rural roadway was constructed. The road consisted of segments featuring extra-urban and village driving environments with varying intersection density level. Participants were recruited to drive through this virtual driving environment. Various driver performance measures such as vehicle speed and brake and gas pedal usage were collected from the experiment and then processed for further analysis. Results indicate an increase in driver’s perceived risk when the intersection density increased, according with the findings from the accident prediction modeling literature. However, at the same time, this driving simulator study revealed some interesting insights about oscillating perceived risk among drivers in the case of mid-level intersection separation distances. Beyond the accident research domain, findings from this study can also be useful for engineers and transportation agencies associated with access management to make more informed decisions. Full article

(This article belongs to the Special Issue Transportation Safety and Pavement Management)

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13 pages, 3738 KiB

Open AccessArticle

Coupling Virtual Reality Simulator with Instantaneous Emission Model: A New Method for Estimating Road Traffic Emissions

by Maria Rosaria De Blasiis, Chiara Ferrante, Fulvio Palmieri and Valerio Veraldi

Sustainability 2022, 14(11), 6793; https://doi.org/10.3390/su14116793 - 01 Jun 2022

Cited by 1 | Viewed by 1334

Abstract

The article presents a new methodology for traffic emissions modeling by coupled the use of dynamic emissions models with a virtual reality driving simulator. The former allows the drivers’ behavior to be studied through a virtual reality driving test, focusing the attention on how traffic flow conditions combined with road geometrical characteristics influence the driving behavior. The latter is used to model the instantaneous vehicle emissions, starting from the driving data provided by the driving simulator. The article analyzes the relationship among three factors: the driving behavior, the pollutant emissions, and the traffic flow condition. The results highlight the influence of the drivers’ behavior on fuel consumption and emissions factors. Under high traffic flow, despite the reduction of the average vehicle speed, the average emissions level increases due to the increased vehicle accelerations and decelerations, which influence the behavior of the engine and the aftertreatment system. The proposed approach points out the relationship between vehicle emissions and drivers’ behavior. Since the coupling among instantaneous emissions modeling and geometry-functionality conditions of the road reveals important elements that traditional approaches miss, the proposed method provides a new way to increase the efficiency of road design and management, from the environmental point of view. Full article

(This article belongs to the Special Issue Transportation Safety and Pavement Management)

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17 pages, 3754 KiB

Open AccessArticle

Short-Term Traffic Speed Forecasting Model for a Parallel Multi-Lane Arterial Road Using GPS-Monitored Data Based on Deep Learning Approach

by Quang Hoc Tran, Yao-Min Fang, Tien-Yin Chou, Thanh-Van Hoang, Chun-Tse Wang, Van Truong Vu, Thi Lan Huong Ho, Quang Le and Mei-Hsin Chen

Sustainability 2022, 14(10), 6351; https://doi.org/10.3390/su14106351 - 23 May 2022

Cited by 9 | Viewed by 1983

Abstract

Traffic speed forecasting in the short term is one of the most critical parts of any intelligent transportation system (ITS). Accurate speed forecasting can support travelers’ route choices, traffic guidance, and traffic control. This study proposes a deep learning approach using long short-term memory (LSTM) network with tuning hyper-parameters to forecast short-term traffic speed on an arterial parallel multi-lane road in a developing country such as Vietnam. The challenge of mishandling the location data of vehicles on small and adjacent multi-lane roads will be addressed in this study. To test the accuracy of the proposed forecasting model, its application is illustrated using historical voyage GPS-monitored data on the Le Hong Phong urban arterial road in Haiphong city of Vietnam. The results indicate that in comparison with other models (e.g., traditional models and convolutional neural network), the best performance in terms of root mean square error (RMSE), mean absolute error (MAE), and median absolute error (MDAE) is obtained by using the proposed model. Full article

(This article belongs to the Special Issue Transportation Safety and Pavement Management)

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12 pages, 1866 KiB

Open AccessArticle

Foamed Bitumen Mixtures for Road Construction Made with 100% Waste Materials: A Laboratory Study

by Nicola Baldo, Fabio Rondinella, Fabiola Daneluz and Marco Pasetto

Sustainability 2022, 14(10), 6056; https://doi.org/10.3390/su14106056 - 17 May 2022

Cited by 12 | Viewed by 2513

Abstract

Nowadays, budget restrictions for road construction, management, and maintenance require innovative solutions to guarantee the user acceptable service levels respecting environmental requirements. Such goals can be achieved by the re-use of various waste materials at the end of their service life in the pavement structure, therefore avoiding their disposal in landfill. At the same time, significant savings are achieved on natural aggregate by replacing it with such waste materials, improving the economic and environmental sustainability of road constructions. The purpose of this study is to discuss a laboratory investigation about foamed bitumen-stabilized mixtures for road foundation layers, in which the aggregate structure was entirely made up of industrial by-products and civil wastes, namely metallurgical slags such as electric arc furnace (EAF) and ladle furnace (LF) slags, coal fly (CF) ash, bottom ash from municipal solid waste incineration (MSWI), glass waste (GW) and reclaimed asphalt pavement (RAP). Combining these recycled aggregates in different proportions, six foamed bitumen mixtures were produced and investigated in terms of indirect tensile strength, stiffness modulus, and fatigue resistance. The leaching test carried out on the waste materials considered did not show any toxicological issue and the best foamed bitumen mixture’s composition was characterized by 20% of EAF slags, 10% of LF slags, 20% of MSWI ash, 10% of CF ash, 20% of GW, and 20% of RAP. Its mechanical characterization presented a dry indirect tensile strength at 25 °C of 0.62 MPa (well above the Italian technical acceptance limits), a stiffness modulus at 25 °C equal to 6171 MPa, and a number of cycles to failure at 20 °C equal to 6989 for a stress level of 300 kPa. Full article

(This article belongs to the Special Issue Transportation Safety and Pavement Management)

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15 pages, 3700 KiB

Open AccessArticle

How to Integrate On-Street Bikeway Maintenance Planning Policies into Pavement Management Practices

by Carlos M. Chang, Marketa Vavrova and Syeda Lamiya Mahnaz

Sustainability 2022, 14(9), 4986; https://doi.org/10.3390/su14094986 - 21 Apr 2022

Cited by 1 | Viewed by 1598

Abstract

As more on-road bikeways are built, the timely application of maintenance treatments becomes critical to ensure safe and comfortable conditions for bicyclists. Longitudinal and transverse cracks that evolve to potholes, rough cut utility patching, raveling, and weathering are flexible pavement distresses that pose safety threats to bicyclists. Faulting and spalling are also safety hazards to bicyclists on rigid pavements. Despite of the need to adopt preventive maintenance policies to preserve on-street bikeways in good condition, bikeway maintenance practices are mostly reactive. The main contribution of this paper is to integrate bikeways maintenance criteria into a policy planning approach for pavement management practices. This planning approach articulates inventory data, condition assessment, maintenance treatment selection, budget needs, funding prioritization, and reports for the implementation of enhanced pavement management systems. Information Technology Systems (ITS) should also support data collection and analysis in the implementation of an integrated maintenance approach. With the adoption of ITS tools, traffic flow, space occupancy and congestion information can be registered in real-time for efficient management. As a result, transportation agencies, metropolitan planning organizations, and cities should make better-informed maintenance decisions for the benefit of all road users. Full article

(This article belongs to the Special Issue Transportation Safety and Pavement Management)

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21 pages, 9248 KiB

Open AccessArticle

Use of Parallel ResNet for High-Performance Pavement Crack Detection and Measurement

by Zhun Fan, Huibiao Lin, Chong Li, Jian Su, Salvatore Bruno and Giuseppe Loprencipe

Sustainability 2022, 14(3), 1825; https://doi.org/10.3390/su14031825 - 05 Feb 2022

Cited by 37 | Viewed by 3623

Abstract

In the process of road pavement health and safety assessment, crack detection plays a pivotal role in a preventive maintenance strategy. Recently, Convolutional Neural Networks (CNNs) have been applied to automatically identify the cracks on concrete pavements. The effectiveness of a CNN-based road crack detection and measurement method depends on several factors, including the image segmentation of cracks with complex topology, the inference of noises with similar texture to the distress, and the sensitivity to thin cracks. The presence of shadows, strong light reflections, and road markings can also severely affect the accuracy in detection and measurement. In this study, a review of the state-of-the-art CNN methods for crack identification is presented, paying attention to existing limitations. Then, a novel deep residual convolutional neural network (Parallel ResNet) is proposed with the aim of creating a high-performance pavement crack detection and measurement system. The challenge and special feature of Parallel ResNet is to remove the noise inference, identifying even thin and complex cracks correctly. The performance of Parallel ResNet has been investigated on two publicly available datasets (CrackTree200 and CFD), comparing it with that of competing methods suggested in the literature. Parallel ResNet reached the maximum scores in Precision (94.27%), Recall (92.52%), and F1 (93.08%) using the CrackTree200 dataset. Similarly, for the CFD dataset the novel method achieved high values in Precision (96.21%), Recall (95.12%), and F1 (95.63%). Based on the crack detection and image recognition results, mathematical morphology was then used to further minimize noise and accurately segment the road diseases, obtaining the outer contours of the connected domain in crack images. Therefore, crack skeletons have been extracted to measure the distress length, width, and area on images of rigid pavements. The experimental results show that Parallel ResNet can effectively minimize noise to obtain the geometry of cracks. The results of crack characteristic measurements are accurate and Parallel ResNet can be assumed as a reliable method in pavement crack image analysis, in order to plan the best road maintenance strategy. Full article

(This article belongs to the Special Issue Transportation Safety and Pavement Management)

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23 pages, 84852 KiB

Open AccessArticle

Monitor Activity for the Implementation of a Pavement—Management System at Cagliari Airport

by Paola Di Mascio, Antonella Ragnoli, Silvia Portas and Marco Santoni

Sustainability 2021, 13(17), 9837; https://doi.org/10.3390/su13179837 - 01 Sep 2021

Cited by 5 | Viewed by 2356

Abstract

The conditions of airport movement-area pavements play a primary role on safety and regularity of airport operations; for this reason, the aerodrome operator needs to periodically survey their condition and provide their maintenance and rehabilitation in order to ensure the required operational characteristics. To meet these needs efficiently and effectively, the Airport Pavement-Management System (APMS) has proved to be a strategic tool to support decisions, aimed at defining a technically and economically sustainable management plan. This paper aims to investigate the theoretical elements and structure of the APMS; the appropriate methodologies to guarantee a constant updating of the system in all its aspects are presented, focusing on the specific case study of a medium-dimension Italian airport. The article describes the methods and the equipment used for the high-performance surveys and the condition indexes used for collecting and analyzing the data implemented to populate the APMS of Cagliari airport. Two major survey campaigns were carried out: the first in 2016 and the second in 2019. Both surveys were carried out using the same subdivision into sample units, following the ASTM D5340-12 criteria, to correctly compare data collected in different years. In order to sufficiently populate the APMS database, the measured and back-calculated data were stored and integrated using daily acquired pavement reports since 2009 and stored with the specific intention to develop customized decay curves for Cagliari Airport pavements. Preliminary results on the sustainable use of the APMS were reported even with data collected in a limited period and successfully applied to runway flexible pavement. Full article

(This article belongs to the Special Issue Transportation Safety and Pavement Management)

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18 pages, 3624 KiB

Open AccessArticle

Surrogate Safety Measures Prediction at Multiple Timescales in V2P Conflicts Based on Gated Recurrent Unit

by Matteo Miani, Matteo Dunnhofer, Christian Micheloni, Andrea Marini and Nicola Baldo

Sustainability 2021, 13(17), 9681; https://doi.org/10.3390/su13179681 - 28 Aug 2021

Cited by 1 | Viewed by 1918

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 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)

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12 pages, 15008 KiB

Open AccessArticle

Proposal and Implementation of a Heliport Pavement Management System: Technical and Economic Comparison of Maintenance Strategies

by Paola Di Mascio, Alessio Antonini, Piero Narciso, Antonio Greto, Marco Cipriani and Laura Moretti

Sustainability 2021, 13(16), 9201; https://doi.org/10.3390/su13169201 - 17 Aug 2021

Cited by 5 | Viewed by 1939

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 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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17 pages, 3610 KiB

Open AccessEditor’s ChoiceArticle

A Machine Learning Approach to Determine Airport Asphalt Concrete Layer Moduli Using Heavy Weight Deflectometer Data

by Nicola Baldo, Matteo Miani, Fabio Rondinella and Clara Celauro

Sustainability 2021, 13(16), 8831; https://doi.org/10.3390/su13168831 - 06 Aug 2021

Cited by 17 | Viewed by 2504

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 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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11 pages, 4860 KiB

Open AccessArticle

Applying Machine Learning to Develop Lane Control Principles for Mixed Traffic

by Tien-Pen Hsu, Ku-Lin Wen and Taiyi Zhang

Sustainability 2021, 13(14), 7656; https://doi.org/10.3390/su13147656 - 08 Jul 2021

Viewed by 1717

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 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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27 pages, 20622 KiB

Open AccessArticle

Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models

by Rita Justo-Silva, Adelino Ferreira and Gerardo Flintsch

Sustainability 2021, 13(9), 5248; https://doi.org/10.3390/su13095248 - 07 May 2021

Cited by 43 | Viewed by 7543

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 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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