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Special Issue "Latest Research on Safety Improvements for Sustainable Transportation Systems"

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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: 1 October 2023 | Viewed by 2643

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Special Issue Editors

Dr. Maxim A. Dulebenets

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Guest Editor

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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Guest Editor

School of Automobile and Mechanical Engineering, Changsha University of Science and Technology, Changsha, China
Interests: Impact biomechanics; traffic and vehicle crash safety; traffic accident analysis
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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, Hong Kong, China
Interests: sustainable development; impacts of climate change; regional development; sustainability issues; green supply chain management; Industry 4.0
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Dr. Guangdong Tian

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Guest Editor

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 2200 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 (4 papers)

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Research

Open AccessArticle

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

Jun Tu

Liangdong Wan

and

Zijiao Sun

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

Viewed by 547

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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Open AccessArticle

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

and

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

Viewed by 384

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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Open AccessArticle

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

Maryam Moradi

and

Gabriel J. Assaf

Sustainability 2023, 15(2), 1157; https://doi.org/10.3390/su15021157 - 07 Jan 2023

Viewed by 657

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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Open AccessArticle

Frontal Vehicular Crash Energy Management Using Analytical Model in Multiple Conditions

and

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

Viewed by 598

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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