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Sustainable Transportation – Smart Transport Systems, Road Network Dynamics and Traffic Planning

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

Deadline for manuscript submissions: closed (15 November 2022) | Viewed by 5836

Special Issue Editors

Dr. Yonas Zewdu Ayele

E-Mail Website
Guest Editor
Faculty of Engineering, Østfold University College, 1757 Halden, Norway
Interests: sustainable transport planning; smart transport systems; road network dynamics; impact assessment of road infrastructure; autonomous vehicles (AVs); privacy and cybersecurity challenges of AVs; autonomous mobility; resilience assessment of critical infrastructures; (dynamic) Bayesian networks; causal modeling; risk analysis; event sequence diagrams and influence diagrams; cost-effectiveness analysis; risks and opportunities for sustainable mobility; socio-economic analysis; decision support tools
Dr. Enrique Lopez Droguett

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Chile, 1058 Santiago, Chile
Interests: reliability; risk; big data; IA in PHM; uncertainty

Special Issue Information

Dear Colleagues,

Extreme weather events and rising populations are straining the existing (and often inadequate) transportation infrastructure in cities around the world. Many of the transport infrastructure in Europe and the U.S. are at the end of their lifespan. This impacts human safety and weakens economies. Additional problems linked to climate variability are expected to increase the threat to transport infrastructure, urban settlements, and their natural and built environments. In this regard, the need for smart and sustainable alternatives for transportation is becoming immense. Sustainable transport planning, as a way toward an integrated, technology-led, and user- and environment-friendly system, is essential for effectively and adaptively addressing societal challenges, simultaneously providing human well-being and biodiversity benefits.

Sustainable infrastructure development promotes resilient and smart transport systems that meet the needs of the present generations without compromising the ability of future generations to meet their own needs. It contains within it two key concepts: (i) the concept of needs, in particular the essential needs of the world's poor, to which overriding priority should be given; and (ii) the idea of limitations imposed by the state of technology and social organization on the environment's ability to meet present and future needs. In transforming the infrastructure development and transport planning to sustainable transport planning and infrastructure development, more than USD 50 trillion will be invested globally in new urban infrastructure and transport systems by the year 2030. However, with rapid urbanization and the growing needs for infrastructure, global cities face huge challenges to provide sustainable infrastructure and smart transport systems, such as connected autonomous vehicles (AVs). Some of the key benefits of connected AVs are the new opportunities to solve mobility and environmental problems, facilitating better mobility, allowing people with restricted access to public transport, posing new challenges in restructuring the transport infrastructures, potentially reducing traffic related accidents, and increasing mobility and reducing congestion. However, there are still significant barriers to integrating AVs into sustainable transport systems due to technical conditions, moral and ethical aspects, legal barriers, and privacy and cybersecurity challenges.

The objective of this Special Issue is to document research contributions in the field of sustainable infrastructure development and smart and sustainable transport planning to significantly contribute to overcoming the deficiencies of the current transport systems. In particular, we look for interdisciplinary contributions to sustainable infrastructure development and smart transport systems (including autonomous mobility) that are relevant for achieving the UN Sustainable Development Goals, Kyoto Protocol, and the Paris Agreement on climate change mitigation and adaptation. The Special Issue will also explore the significant benefits of adapting and connecting AVs to reduce time and costs, and establishing the research needs associated with the processing of the massive and complex data produced by such autonomous technologies.

Dr. Yonas Zewdu Ayele
Dr. Enrique Lopez Droguett
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

  • sustainable transport planning
  • smart transport systems
  • road network dynamics
  • impact assessment of road infrastructures
  • autonomous vehicles (AVs)
  • privacy and cybersecurity challenges of AVs
  • autonomous mobility
  • resilience assessment of critical infrastructures
  • (dynamic) Bayesian networks
  • causal modeling
  • risk analysis
  • cost-effectiveness (benefit) analysis
  • risks and opportunities for sustainable mobility
  • socio-economic analysis
  • decision support tools

Published Papers (3 papers)

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Research

11 pages, 865 KiB  
Article
Reversible Lane Optimization of the Urban Road Network Considering Adjustment Time Constraints
by Jianrong Cai, Zhixue Li, Yinghong Xiao, Zhaoming Zhou, Qiong Long, Jie Yu, Jinfan Zhang and Lei Zhang
Sustainability 2023, 15(2), 1381; https://doi.org/10.3390/su15021381 - 11 Jan 2023
Viewed by 1095
Abstract
Reversible lanes constitute an important solutions for sustainable transportation, with the aim to solve the practical problem of reversible lane optimization of urban road networks constrained by adjustment time. Considering the relationship between the number of lanes and the capacity of sections, a [...] Read more.
Reversible lanes constitute an important solutions for sustainable transportation, with the aim to solve the practical problem of reversible lane optimization of urban road networks constrained by adjustment time. Considering the relationship between the number of lanes and the capacity of sections, a mixed-integer bilevel programming model of reversible lane optimization constrained by adjustment time is constructed in order to minimize the total travel time of the system. The results show that the model can effectively obtain the optimal strategy for any number of reversible sections subject to adjustment time constraints. With the increase of the number of reversible sections that can be optimized within the adjustment time, the cumulative reduced system time increases monotonically and the road network optimization effect improves, but as a whole, the optimization effect of the newly added reversible sections in each stage shows a decreasing trend. When the number of reversible sections that can be optimized within the adjustment time reaches a certain number, increasing the number of reversible sections will have a limited further effect on the overall system. For the reversible lane optimization problem of urban road networks, only efficient reversible sections need to be optimized to achieve a good optimization effect. Full article
(This article belongs to the Special Issue Sustainable Transportation – Smart Transport Systems, Road Network Dynamics and Traffic Planning)
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17 pages, 5156 KiB  
Article
Temporary Reversible Lane Design Based on Bi-Level Programming Model during the Winter Olympic Games
by Weiqi Hong, Zishu Yang, Xu Sun, Jianyu Wang and Pengpeng Jiao
Sustainability 2022, 14(8), 4780; https://doi.org/10.3390/su14084780 - 15 Apr 2022
Cited by 2 | Viewed by 1528
Abstract
When the Winter Olympic Games were held, several roads were divided into exclusive lanes for the Winter Olympics to ensure the smooth passage of Winter Olympic vehicles. This reduced the number of lanes available for private vehicles, which caused a temporary tidal traffic [...] Read more.
When the Winter Olympic Games were held, several roads were divided into exclusive lanes for the Winter Olympics to ensure the smooth passage of Winter Olympic vehicles. This reduced the number of lanes available for private vehicles, which caused a temporary tidal traffic phenomenon that led to traffic congestion and increased exhaust emissions. Temporary reversible lanes were added to the object lane to alleviate the temporary tide traffic phenomenon. A bi-level programming model was developed based on the principle of the minimum construction cost and the minimum total travel time of the road network. Meanwhile, three heuristics algorithms were used to solve the problem. The results show that the reasonable addition of temporary reversible lanes during the Olympic Games can reduce the total system travel cost, solve the temporary tidal traffic phenomenon, and alleviate traffic congestion. Full article
(This article belongs to the Special Issue Sustainable Transportation – Smart Transport Systems, Road Network Dynamics and Traffic Planning)
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27 pages, 112748 KiB  
Article
UAV-Based Bridge Inspection via Transfer Learning
by Mostafa Aliyari, Enrique Lopez Droguett and Yonas Zewdu Ayele
Sustainability 2021, 13(20), 11359; https://doi.org/10.3390/su132011359 - 14 Oct 2021
Cited by 13 | Viewed by 2425
Abstract
As bridge inspection becomes more advanced and more ubiquitous, artificial intelligence (AI) techniques, such as machine and deep learning, could offer suitable solutions to the nation’s problems of overdue bridge inspections. AI coupling with various data that can be captured by unmanned aerial [...] Read more.
As bridge inspection becomes more advanced and more ubiquitous, artificial intelligence (AI) techniques, such as machine and deep learning, could offer suitable solutions to the nation’s problems of overdue bridge inspections. AI coupling with various data that can be captured by unmanned aerial vehicles (UAVs) enables fully automated bridge inspections. The key to the success of automated bridge inspection is a model capable of detecting failures from UAV data like images and films. In this context, this paper investigates the performances of state-of-the-art convolutional neural networks (CNNs) through transfer learning for crack detection in UAV-based bridge inspection. The performance of different CNN models is evaluated via UAV-based inspection of Skodsberg Bridge, located in eastern Norway. The low-level features are extracted in the last layers of the CNN models and these layers are trained using 19,023 crack and non-crack images. There is always a trade-off between the number of trainable parameters that CNN models need to learn for each specific task and the number of non-trainable parameters that come from transfer learning. Therefore, selecting the optimized amount of transfer learning is a challenging task and, as there is not enough research in this area, it will be studied in this paper. Moreover, UAV-based bridge inception images require specific attention to establish a suitable dataset as the input of CNN models that are trained on homogenous images. However, in the real implementation of CNN models in UAV-based bridge inspection images, there are always heterogeneities and noises, such as natural and artificial effects like different luminosities, spatial positions, and colors of the elements in an image. In this study, the effects of such heterogeneities on the performance of CNN models via transfer learning are examined. The results demonstrate that with a simplified image cropping technique and with minimum effort to preprocess images, CNN models can identify crack elements from non-crack elements with 81% accuracy. Moreover, the results show that heterogeneities inherent in UAV-based bridge inspection data significantly affect the performance of CNN models with an average 32.6% decrease of accuracy of the CNN models. It is also found that deeper CNN models do not provide higher accuracy compared to the shallower CNN models when the number of images for adoption to a specific task, in this case crack detection, is not large enough; in this study, 19,023 images and shallower models outperform the deeper models. Full article
(This article belongs to the Special Issue Sustainable Transportation – Smart Transport Systems, Road Network Dynamics and Traffic Planning)
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