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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 (1 December 2023) | Viewed by 18309

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Special Issue Editors

Dr. Maxim A. Dulebenets

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College of Engineering, Florida A&M University-Florida State University, Tallahassee, FL 32310-6046, USA
Interests: operations research; simulation modeling; optimization; NP-hard problems; liner shipping scheduling; evolutionary computation; mathematical programming; hybrid algorithms; metaheuristics; transportation engineering; GPS data processing
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Dr. Amir M. Fathollahi-Fard

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Peter B. Gustavson School of Business, University of Victoria, P.O. Box 1700, Victoria, BC, Canada
Interests: supply chain management; healthcare systems; sustainable logistics and production management; optimization algorithms; heuristics; metaheuristics
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Dr. Danqi Wang

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School of Automobile and Mechanical Engineering, Changsha University of Science and Technology, Changsha, China
Interests: conceptual design; structural safety and energy saving design; multidisciplinary optimization
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Dr. Yui-Yip Lau

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Guest Editor

Division of Business and Hospitality Management, College of Professional and Continuing Education, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Interests: cruise ships; ferries; impacts of climate change; shipping education and training; transport history; sustainability issues; resilient supply chain management; health logistics; human remains and regional development
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Dr. Guangdong Tian

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School of Mechanical-Electrical and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Interests: remanufacturing and green manufacturing; green logistics and transportation; intelligent inspection and automotive repair
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of the transportation industry across all the major modes (including road, rail, sea, and air), the number of accidents has significantly increased. Many of these accidents are caused by human factors (e.g., lack of driver attention that causes collisions of roadway vehicles at busy intersections; insufficient experience of ship crew members that causes accidents for the cruise industry; inexperienced pilots taking risky maneuvers during flights). A lack of proper maintenance activities and up-to-date equipment could increase the risk of accidents as well. Some causes of accidents are rather difficult to control (e.g., accidents caused by hurricanes, tsunamis, tornadoes, wildfire, earthquakes, etc.). Scientists and industry representatives are now seeking advanced methods and technologies that could be used to improve the safety of transportation systems. These methods and technologies include, but are not solely limited to, big data analytics, real-time collision warnings, soft computing methods, artificial intelligence, new types of algorithms, new types of materials for safer transportation, and autonomous agents. Furthermore, increasing attention is now being dedicated to enhancing the resilience of transportation systems. Resilient transportation systems are able to quickly recover from the effects of disruptions and prevent the occurrence of additional accidents.

This Special Issue focuses on the latest research outcomes related to safety improvements for different types of transportation systems. The scope of this Special Issue includes, but is not limited to, the following topics:

Accident data analysis and multifaceted feature extraction;
Injury bio-mechanism analysis of passengers under different accident conditions;
High-performance materials and structural design for safer transportation systems;
Multidisciplinary optimization methods to improve the crashworthiness of structures;
Safety protection for vulnerable transportation users;
Advanced safety devices and support systems for passengers;
Safety management and organizational behavior;
Optimization algorithms (e.g., heuristics and metaheuristics) and artificial intelligence (e.g., machine learning methods, neural computing models) for accident analysis and prevention;
Innovative approaches for improving sustainability and resilience of transportation systems;
Human errors and autonomous transportation systems;
Multi-disciplinary studies addressing safety issues associated with transportation systems.

We look forward to receiving your contributions.

You may choose our Joint Special Issue in Future Transportation.

Dr. Maxim A. Dulebenets
Dr. Amir M. Fathollahi-Fard
Dr. Danqi Wang
Dr. Yui-yip Lau
Dr. Guangdong Tian
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 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 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

transportation
road safety
technology

Published Papers (11 papers)

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Research

18 pages, 2197 KiB

Open AccessArticle

Understanding Active Transportation to School Behavior in Socioeconomically Disadvantaged Communities: A Machine Learning and SHAP Analysis Approach

by Bita Etaati, Arash Jahangiri, Gabriela Fernandez, Ming-Hsiang Tsou and Sahar Ghanipoor Machiani

Sustainability 2024, 16(1), 48; https://doi.org/10.3390/su16010048 - 20 Dec 2023

Viewed by 827

Abstract

Active Transportation to School (ATS) offers numerous health benefits and is considered an affordable option, especially in disadvantaged neighborhoods. The US Centers for Disease Control and Prevention (CDC) advises 60 min of daily physical exercise for children aged 6 to 17, making ATS a compelling approach to promote a healthier lifestyle among students. Initiated in 2005 by the US Department of Transportation (DOT), the Safe Routes to School (SRTS) program aims to foster safe and regular walking and biking to school for students. This paper examines students’ travel behavior using SRTS survey data and assesses the program’s effectiveness in promoting ATS in Chula Vista, California. Employing machine learning algorithms (random forest, logistic regression, and support vector machines) to predict students’ likelihood to walk to school, it utilizes SHAP (SHapley Additive exPlanations) to pinpoint significant variables influencing ATS across all models. SHAP underscores critical factors affecting transportation choices to school, highlighting the importance of home-to-school distance, with shorter distances positively impacting active transportation. However, only half of students within schools’ walking distance opted to walk to school, underscoring the necessity of addressing parental safety concerns, including factors such as crime rates and traffic speed along the route. Full article

(This article belongs to the Special Issue Latest Research on Safety Improvements for Sustainable Transportation Systems)

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17 pages, 1576 KiB

Open AccessArticle

Limited Response of Curve Safety Level to Friction Factor and Superelevation Variation under Repeated Traffic Loads

by Jinliang Xu, Miao Jia, Chao Gao and Wenzhen Lv

Sustainability 2023, 15(24), 16923; https://doi.org/10.3390/su152416923 - 17 Dec 2023

Viewed by 848

Abstract

Although road horizontal curves are high-risk sections for accidents, current road safety assessments often neglect the dynamic evolution of superelevation and the friction factor. The connotation for road safety level was clarified by examining the significance of road factors in traffic safety through the systemic characteristics of roads. Among these characteristics, curve safety level is determined by the ratio of the supply and demand of the lateral friction factor. On the basis of international standards and specifications, this study clarified the design supply and demand of friction factors for curve by considering the distribution of tangential and lateral friction factors. Expanding on the steady-state bicycle model while accounting for road geometric parameters and vehicle operation characteristics, the lateral friction factor demanded for vehicles was quantified. Meanwhile, the characteristics of the friction factor supplied and the superelevation variation were analyzed by using the road service life as a variable, along with their influence on the actual supply of the friction factor and the curve safety level. The results of the analysis indicate a rapid decrease in curve safety level during the first two years of road utilization, followed by a slower declining trend, with a significant 27% reduction in curve safety level by the end of the second year. Furthermore, the decline in the curve safety level is mainly attributed to variations in the road surface friction factor, whereas the influence of superelevation variation on the curve safety level is restricted. In the absence of maintenance interventions, the curve safety level will decrease by over 30% after three years of operation. Controlling operational speed is one of the effective measures for ensuring traffic safety. Meanwhile, the impact of the friction factor and the superelevation variation on the curve safety level accumulates over time, thus causing drivers to have difficulty perceiving these alterations. Therefore, dynamic safety evaluations that account for the fluctuation in the friction factor and superelevation induced by repetitive vehicle loading must be undertaken. Full article

(This article belongs to the Special Issue Latest Research on Safety Improvements for Sustainable Transportation Systems)

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29 pages, 4404 KiB

Open AccessArticle

Timetable Rescheduling Using Skip-Stop Strategy for Sustainable Urban Rail Transit

by Zhichao Cao, Yuqing Wang, Zihao Yang, Changjun Chen and Silin Zhang

Sustainability 2023, 15(19), 14511; https://doi.org/10.3390/su151914511 - 5 Oct 2023

Cited by 1 | Viewed by 1089

Abstract

Unanticipated events inevitably occur in daily urban rail transit operations, disturbing the scheduled timetable. Despite the mild delay, the busy operation system probably tends to worsen a larger disturbance and even lead to a knock-on disruption if no rescheduling is timely carried out. We propose a bi-objective mixed-integer linear programming model (MILP) that employs the skip-stop operation strategy to eliminate unscheduled delays. This model addresses two distinct, yet interconnected objectives. Firstly, it aims to minimize the difference between the plan and the actual operation. Secondly, it strives to minimize the number of left-behind passengers. In order to resolve this MILP problem, we devised a Pareto-based genetic algorithm (GA). Based on the case study, we certify the superior effectiveness with comparisons to the whale optimization algorithm and the epsilon constraint method. The outcomes affirm that our model has the potential to reduce the total delay time of the line by 44.52% at most compared with the traditional all-stop running adjustment model. The optimal scheme saved 6.08% of the total costs based on a trade-off between operators’ interests and passenger satisfaction. Full article

(This article belongs to the Special Issue Latest Research on Safety Improvements for Sustainable Transportation Systems)

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16 pages, 2387 KiB

Open AccessArticle

Investigating Factors for Travelers’ Parking Behavior Intentions in Changchun, China, under the Influence of Smart Parking Systems

by Yunxiang Zhang, Xianmin Song, Pengfei Tao, Haitao Li, Tianshu Zhan and Qian Cao

Sustainability 2023, 15(15), 11685; https://doi.org/10.3390/su151511685 - 28 Jul 2023

Viewed by 978

Abstract

Unraveling the determinants of travelers’ parking behavior intentions is critical to the widespread adoption of smart parking systems (SPSs), which hold the promise of greatly enhancing parking efficiency and optimizing resource allocation within urban spaces. Our study pioneers the use of an integrated methodology combining structural equation modeling (SEM) and hierarchical regression modeling (HRM) to dissect the complex interplay of these determinants. We found that, in the structural equation model, social influence notably stood out as having the most significant impact on the intention to utilize SPSs. Notably, while perceived privacy concerns may have ranked lower in terms of influence among these factors, their role was relatively crucial, particularly given the contemporary emphasis on data security. Moreover, within the hierarchical regression model, driving experience was found to play a crucial role in determining the intention to use SPSs. Equally important, our research revealed a divergence in parking intentions between individuals with children and those without. This points towards the imperative need for personalized strategies that can cater to the diverse requirements of different user demographics. This research offers guidance for operators of SPSs aiming to formulate targeted approaches. Full article

(This article belongs to the Special Issue Latest Research on Safety Improvements for Sustainable Transportation Systems)

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25 pages, 10281 KiB

Open AccessArticle

A Feature Fusion Method for Driving Fatigue of Shield Machine Drivers Based on Multiple Physiological Signals and Auto-Encoder

by Kun Liu, Guoqi Feng, Xingyu Jiang, Wenpeng Zhao, Zhiqiang Tian, Rizheng Zhao and Kaihang Bi

Sustainability 2023, 15(12), 9405; https://doi.org/10.3390/su15129405 - 12 Jun 2023

Cited by 1 | Viewed by 1172

Abstract

The driving fatigue state of shield machine drivers directly affects the safe operation and tunneling efficiency of shield machines during metro construction. To cope with the problem that it is challenging to simulate the working conditions and operation process of shield machine drivers using driving simulation platforms and that the existing fatigue feature fusion methods usually show low recognition accuracy, shield machine drivers at Shenyang metro line 4 in China were taken as the research subjects, and a multi-modal physiological feature fusion method based on an L2-regularized stacked auto-encoder was designed. First, the ErgoLAB cloud platform was used to extract the combined energy feature (E), the reaction time, the HRV (heart rate variability) time-domain SDNN (standard deviation of normal-to-normal intervals) index, the HRV frequency-domain LF/HF (energy ratio of low frequency to high frequency) index and the pupil diameter index from EEG (electroencephalogram) signals, skin signals, pulse signals and eye movement data, respectively. Second, the physiological signal characteristics were extracted based on the WPT (wavelet packet transform) method and time–frequency analysis. Then, a method for driving fatigue feature fusion based on an auto-encoder was designed aiming at the characteristics of the L2-regularization method to solve the over-fitting problem of small sample data sets in the process of model training. The optimal hyper-parameters of the model were verified with the experimental method of the control variable, which reduces the loss of multi-modal feature data in compression fusion and the information loss rate of the fused index. The results show that the method proposed outperforms its competitors in recognition accuracy and can effectively reduce the loss rate of deep features in existing decision-making-level fusion. Full article

(This article belongs to the Special Issue Latest Research on Safety Improvements for Sustainable Transportation Systems)

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17 pages, 1559 KiB

Open AccessArticle

ROAD: Robotics-Assisted Onsite Data Collection and Deep Learning Enabled Robotic Vision System for Identification of Cracks on Diverse Surfaces

by Renu Popli, Isha Kansal, Jyoti Verma, Vikas Khullar, Rajeev Kumar and Ashutosh Sharma

Sustainability 2023, 15(12), 9314; https://doi.org/10.3390/su15129314 - 9 Jun 2023

Cited by 5 | Viewed by 1594

Abstract

Crack detection on roads is essential nowadays because it has a significant impact on ensuring the safety and reliability of road infrastructure. Thus, it is necessary to create more effective and precise crack detection techniques. A safer road network and a better driving experience for all road users can result from the implementation of the ROAD (Robotics-Assisted Onsite Data Collecting) system for spotting road cracks using deep learning and robots. The suggested solution makes use of a robot vision system’s capabilities to gather high-quality data about the road and incorporates deep learning methods for automatically identifying cracks. Among the tested algorithms, Xception stands out as the most accurate and predictive model, with an accuracy of over 90% during the validation process and a mean square error of only 0.03. In contrast, other deep neural networks, such as DenseNet201, InceptionResNetV2, MobileNetV2, VGG16, and VGG19, result in inferior accuracy and higher losses. Xception also achieves high accuracy and recall scores, indicating its capability to accurately identify and classify different data points. The high accuracy and superior performance of Xception make it a valuable tool for various machine learning tasks, including image classification and object recognition. Full article

(This article belongs to the Special Issue Latest Research on Safety Improvements for Sustainable Transportation Systems)

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20 pages, 6284 KiB

Open AccessArticle

How Will Autonomous Vehicles Decide in Case of an Accident? An Interval Type-2 Fuzzy Best–Worst Method for Weighting the Criteria from Moral Values Point of View

by Burak Can Altay, Abdullah Erdem Boztas, Abdullah Okumuş, Muhammet Gul and Erkan Çelik

Sustainability 2023, 15(11), 8916; https://doi.org/10.3390/su15118916 - 1 Jun 2023

Cited by 2 | Viewed by 1854

Abstract

The number of studies on Autonomous Vehicle (AV) ethics discussing decision-making algorithms has increased rapidly, especially since 2017. Many of these studies handle AV ethics through the eye of the trolley problem regarding various moral values, regulations, and matters of law. However, the literature of this field lacks an approach to weighting and prioritizing necessary parameters that need to be considered while making a moral decision to provide insights about AVs’ decision-making algorithms and related legislations as far as we know. This paper bridges the gap in the literature and prioritizes some main criteria indicated by the literature by employing the best–worst method in interval type-2 fuzzy sets based on the evaluations of five experts from different disciplines of philosophy, philosophy of law, and transportation. The criteria included in the weighting were selected according to expert opinions and to the qualitative analysis carried out by coding past studies. The weighing process includes a comparison of four different approaches to the best–worst method. The paper’s findings reveal that social status is the most important criterion, while gender is the least important one. This paper is expected to provide valuable practical insights for Autonomous Vehicle (AV) software developers in addition to its theoretical contribution. Full article

(This article belongs to the Special Issue Latest Research on Safety Improvements for Sustainable Transportation Systems)

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21 pages, 899 KiB

Open AccessArticle

Safety Improvement of Sustainable Coal Transportation in Mines: A Contract Design Perspective

by Jun Tu, Liangdong Wan and Zijiao Sun

Sustainability 2023, 15(3), 2085; https://doi.org/10.3390/su15032085 - 21 Jan 2023

Cited by 2 | Viewed by 1563

Abstract

Considering safety management systems are composed of a coal mine enterprise and a manager, incentive contracts for coal mine production are designed to improve the safety level of coal mine production. Managers must devote costly efforts in terms of both safety and production to increase the output of mines. Based on principal–agent theory, we designed an incentive contract considering moral hazard and a menu of contracts considering moral hazard and adverse selection. The results showed that when an enterprise cannot observe the manager’s efforts, the manager’s risk aversion reduces their production and safety efforts, and the enterprise needs to share its output risk with the manager. When the enterprise cannot observe the manager’s efforts and the cost type of the safety effort, a menu of contracts can be used to screen the manager’s cost type. However, high-cost contracts fail to motivate a high-cost manager and allow the high-cost manager to reduce safety and production efforts. A low-cost manager can obtain positive information rent from an enterprise without changing safety or production efforts. We provide some suggestions and references for the safety management of coal transportation in mines. Full article

(This article belongs to the Special Issue Latest Research on Safety Improvements for Sustainable Transportation Systems)

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15 pages, 13643 KiB

Open AccessArticle

Study on the Cell Magnification Equivalent Method in Out-of-Plane Compression Simulations of Aluminum Honeycomb

by Yuning Qiao, Yong Peng, Ping Cheng, Xuefei Zhou, Fang Wang, Fan Li, Kui Wang, Chao Yu and Honggang Wang

Sustainability 2023, 15(3), 1882; https://doi.org/10.3390/su15031882 - 18 Jan 2023

Cited by 1 | Viewed by 1159

Abstract

The large scale and long calculation times are unavoidable problems in modeling honeycomb structures with large sizes and dense cells. The cell magnification equivalent is the main method to solve those problems. This study finds that honeycomb structures with the same thickness-to-length ratios have the same mechanical properties and energy absorption characteristics. The improved equivalent finite element models of honeycomb structures with the same thickness-to-length ratios were established and validated by experiments. Based on the validated finite element model of the equivalent honeycomb structures, the out-of-plane compression behaviors of honeycomb structures were analyzed by LS-DYNA software. The results show that the performance of honeycomb structures is not equivalent before and after cell magnification. Thus, the cell magnification results were further subjected to CORA (correlation analysis) to determine the magnification time and prove the accuracy of the cell magnification time through drop-weight impact tests. In addition, a first-order decay exponential function (ExpDec1) for predicting cell magnification time was obtained by analyzing the relationship between the cell wall length and the cell magnification time. Full article

(This article belongs to the Special Issue Latest Research on Safety Improvements for Sustainable Transportation Systems)

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17 pages, 3932 KiB

Open AccessArticle

Designing and Building an Intelligent Pavement Management System for Urban Road Networks

by Maryam Moradi and Gabriel J. Assaf

Sustainability 2023, 15(2), 1157; https://doi.org/10.3390/su15021157 - 7 Jan 2023

Cited by 5 | Viewed by 3032

Abstract

Pavement maintenance plays a significant role in megacities. Managing complaints and scheduling road reviews are the two maintenance concerns under the intelligent pavement management system (PMS) plan. In contrast, if the damages are not treated immediately, they will increase over time. By leveraging accurate data from sensors, smart PMS will improve management capability, support sustainability, and drive economic growth in the road network. This research aimed to elaborate on the different modules of an intelligent city pavement network to advance to a sustainable city. First, a 3D mobile light detection and ranging (LiDAR) sensor, accompanied by a camera, was applied as the data collection tool. Although 3D mobile LiDAR data have gained popularity, they lack precise detection of pavement distresses, including cracks. As a result, utilizing RGB imaging may help to detect distresses properly. Two approaches were integrated alongside conducting the data analysis in this paper: (1) ArcGIS pro, developed by Esri Inc., which includes noise removal, digital elevation model (DEM) generation, and pavement and building footprint extraction; (2) the Mechanistic-Empirical Pavement Design Guide (AASHTOWare PMED), which was used to assess site specifications such as traffic, weather, subbase, and current pavement conditions in an effort to design the most appropriate pavement for each road section. For the 3D visualization module, CityEngine (a software from Esri) was used to provide the 3D city model. After implementing the research methodology, we drew the following conclusions: (1) using the AASHTOWare PMED method to make decisions about road maintenance and rehabilitation(M&R) actions can significantly speed up the decision-making process, essentially saving time and money and shortening the project’s duration; and (2) if the road conditions are similar, the smart geographical information system (GIS)-based PMS can make consistent decisions about road M&R strategies, i.e., the interference from human factors is less significant. Full article

(This article belongs to the Special Issue Latest Research on Safety Improvements for Sustainable Transportation Systems)

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18 pages, 4195 KiB

Open AccessArticle

Frontal Vehicular Crash Energy Management Using Analytical Model in Multiple Conditions

by Danqi Wang, Junyuan Zhang, Shihang Wang and Lin Hu

Sustainability 2022, 14(24), 16913; https://doi.org/10.3390/su142416913 - 16 Dec 2022

Viewed by 2163

Abstract

When it comes to frontal vehicular crash development, matching the stiffness of the front-end structures reasonably, i.e., impact energy management, can effectively improve the safety of the vehicle. A multi-condition analytical model for a frontal vehicular crash is constructed by a three-dimensional decomposition theory. In the analytical model, the spring is used to express the equivalent stiffness of the local energy absorption space at the front-end structure. Then based on the analytical model, the dynamic responses and evaluation indexes of the vehicle in MPDB and SOB conditions are derived with the input of the crash pulse decomposition scheme. Comparing the actual vehicle crash data and the calculation results of the proposed solution method, the error is less than 15%, which verifies validity of the modeling and the accuracy of the solution. Finally, based on the solution method in the MPDB and the SOB conditions, the sensitivities of the crash pulse decomposition scheme to evaluation indexes are analyzed to obtain qualitative rules which guide crash energy management. This research reveals the energy absorption principle of the front-end structure during the frontal impact process, and provides an effective optimization method to manage the multiple conditions of the vehicle crash energy such as the FRB (frontal rigid barrier), the MPDB (mobile progressive deformable barrier), and the SOB (small overlap barrier). Full article

(This article belongs to the Special Issue Latest Research on Safety Improvements for Sustainable Transportation Systems)

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