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Sustainability Assessment of Emerging Mobility Technologies

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

Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 3739

Special Issue Editors


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Guest Editor
Qatar Transportation and Traffic Safety Center, College of Engineering, Qatar University, Doha 2713, Qatar
Interests: sustainability assessment; sustainable transportation; complex systems; system dynamics; decision analytics

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Guest Editor
Mechanical and Industrial Engineering, College of Engineering, Qatar University, Doha 2713, Qatar
Interests: industrial ecology; sustainability assessment; circular economy; e-mobility; data analytics

Special Issue Information

Dear Colleagues,

The mobility sector is an important part of economic and social development all around the world as it is one of the main backbones of economies, being responsible for the efficient and safe transportation of passengers and goods. Despite the economic and social benefits we acquire from transportation-related activities, sustainable mobility aims to ensure that negative environmental impacts are minimized. The transportation sector alone is responsible for nearly a quarter of the global greenhouse gas emissions, an important contributor to global climate change, and a major responsible activity for air pollution in cities. Furthermore, the need for transportation-related activities is expected to grow with the increasing population in the world. This leads to a rapidly increasing need for energy sources, which are currently provided by fossil fuels. These facts have forced the global community to find more efficient alternative operations and technologies to transport passengers and goods around the world.

With the increasing concerns associated with transportation-related emissions and air pollution in cities, countries around the world have set up ambitious targets for the electrification of mobility. For instance, automobile manufacturers in Germany, France, and the United Kingdom are planning to stop producing internal combustion engines within the next several decades. Cleaner electric mobility initiatives are one of the most promising solutions for environmental impacts resulting from transportation, and thirty-one countries and some of the states in the United States of America are planning to ban gasoline engine vehicles between 2025 and 2050. In addition, mobility as a service (shared economy applications), compact personal electric mobility devices (e-scooters, smaller compact electric cars), on-demand ride services, autonomous and shared autonomous electric cars, and autonomous trucks and container ships for the delivery of goods are some of the important emerging mobility solutions. Indeed, all of these alternative mobility solutions are expected to improve the efficiency of transportation services, but the environmental, economic, and social impacts have yet to be investigated.

Any emerging technology comes with uncertainties in terms of its social, economic, and environmental impacts on the global society, and history has shown that some of the technological changes have also led to great societal transformation, thus shaping humanity’s future. Understanding these changes, and perceiving or anticipating the potential changes are essential to manage and internalize the maximum benefits out of these technological advancements for a better sustainable global community. In the case of emerging technologies, it is very possible to observe side, ripple, or rebound effects.

In this Special Issue, we would like to welcome high-quality research to understand sustainability impacts of emerging mobility technologies and obtain insights related to potential challenges concerning their implementation, management, and relationship with society, the economy, and the environment.

Appropriate topics of this issue include:

  • integrated life cycle-based sustainability assessment of emerging mobility technologies from social, economic, environmental perspectives.
  • integration of sustainability assessment with big data analytics, machine learning, artificial intelligence, and blockchain for data-driven decision making.
  • applications of novel scenario and uncertainty analysis, simulation, and optimization modelling techniques for prescriptive sustainability analytics.
  • use of process-based, hybrid and multiregional life cycle assessment models for city, regional, national and global-level sustainability assessment.
  • sustainability assessment of circular and sharing economy applications as well as necessary technology and infrastructure requirements for the future of mobility.

Dr. Nuri Cihat Onat
Dr. Murat Kucukvar
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 mobility
  • sustainable transportation
  • sustainability asssessment
  • emerging mobility technologies
  • GHG emissions
  • e-mobility

Published Papers (1 paper)

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Research

20 pages, 8557 KiB  
Article
Predictive Machine Learning Algorithms for Metro Ridership Based on Urban Land Use Policies in Support of Transit-Oriented Development
by Aya Hasan AlKhereibi, Tadesse G. Wakjira, Murat Kucukvar and Nuri C. Onat
Sustainability 2023, 15(2), 1718; https://0-doi-org.brum.beds.ac.uk/10.3390/su15021718 - 16 Jan 2023
Cited by 7 | Viewed by 3122
Abstract
The endeavors toward sustainable transportation systems are a key concern for planners and decision-makers where increasing public transport attractiveness is essential. In this paper, a machine-learning-based predictive modeling approach is proposed for metro ridership prediction, considering the built environment around the stations; it [...] Read more.
The endeavors toward sustainable transportation systems are a key concern for planners and decision-makers where increasing public transport attractiveness is essential. In this paper, a machine-learning-based predictive modeling approach is proposed for metro ridership prediction, considering the built environment around the stations; it is in the best interest of sustainable transport planning to ultimately contribute to the achievement of Sustainable Development Goals (UN-SDGs). A total of twelve parameters are considered as input features including time of day, day of the week, station, and nine types of land use density. Hence, a time-series database is used for model development and testing. Several machine learning (ML) models were evaluated for their predictive performance: ridge regression, lasso regression, elastic net, k-nearest neighbor, support vector regression, decision tree, random forest, extremely randomized trees, adaptive boosting, gradient boosting, extreme gradient boosting, and stacking ensemble learner. Bayesian optimization and grid search are combined with 10-fold cross-validation to tune the hyperparameters of each model. The performance of the developed models was validated based on the test dataset using five quantitative performance measures. The results demonstrated that, among the base learners, the decision tree showed the highest performance with an R2 of 87.4% on the test dataset. KNN and SVR were the second and third-best models among the base learners. Furthermore, the feature importance investigation explains the relative contribution of each type of land use density to the prediction of the metro ridership. The results showed that governmental land use density, educational facilities land use density, and mixed-use density are the three factors that play the most critical role in determining total ridership. The outcomes of this research could be of great help to the decision-making process for the best achievement of sustainable development goals in relation to sustainable transport and land use. Full article
(This article belongs to the Special Issue Sustainability Assessment of Emerging Mobility Technologies)
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