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Sustainability
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Artificial Intelligence and IoT for Sustainable Transportation and Smart Infrastructure
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Artificial Intelligence and IoT for Sustainable Transportation and Smart Infrastructure

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

Deadline for manuscript submissions: 31 October 2026 | Viewed by 834

Special Issue Editor

Dr. Catalin Dumitrescu

E-Mail Website
Guest Editor
Faculty of Electronics, Telecommunications, and Information Technology, National University of Science and Technology Politehnica from Bucharest, 060042 Bucharest, Romania
Interests: fractal analysis; computing science; artificial intelligence; machine learning; forensic analysis; deep learning; simulation modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

IoT and Big Data sensor-based systems offer development solutions for smart cities, smart mobile, and intelligent transportation developed from field-specific services, such as mobility, economy, health, and infrastructure, to interoperability systems using digital structures. Thus, safety and security are the main elements that must be taken into account in the development of sustainable IoT architects based on Big Data.

IoT-based systems are of great interest for the development of the future infrastructures of intelligent cities and urban mobility, involving challenges around real-time operating requirements, precision, scalability, and dependence on large databases as a result of the collection of events.

The combination of IoT and artificial intelligence for decision making are a main element for developing the sustainability of smart city and smart country systems.

This multidisciplinary combination can provide a new perspective on implementing and developing innovative architectures for safe cities and increasing mobility. These architectures include applications such as monitoring and predictions based on IoT, drone, satellite, or urban databases and future urban infrastructures dedicated to autonomous cars. We request research in the field of information systems and artificial intelligence, as well as works based on innovative approaches for the development of intelligent cities and intelligent transport.

Dr. Catalin Dumitrescu
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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 engineering
  • smart infrastructure
  • artificial intelligence
  • machine learning
  • Internet of Things

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Published Papers (1 paper)

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Research

25 pages, 4796 KB  
Article
AI-Driven Predictive Analytics for Sustainable Aviation: Metaheuristic-Optimized XGBoost for Carbon Emission Prediction
by Abdullah Mohamed Salem Elarifi and Wagdi M. S. Khalifa
Sustainability 2026, 18(5), 2246; https://doi.org/10.3390/su18052246 - 26 Feb 2026
Viewed by 434
Abstract
Intelligent transportation systems increasingly rely on artificial intelligence and predictive analytics to achieve sustainability. This study presents Adaptive Weighting, Chaos Theory, and Gaussian Mutation-based RIME algorithm-tuned Extreme Gradient Boosting (ACGRIME-XGBoost), an advanced Artificial Intelligence (AI)-driven framework specifically designed for carbon emission prediction in [...] Read more.
Intelligent transportation systems increasingly rely on artificial intelligence and predictive analytics to achieve sustainability. This study presents Adaptive Weighting, Chaos Theory, and Gaussian Mutation-based RIME algorithm-tuned Extreme Gradient Boosting (ACGRIME-XGBoost), an advanced Artificial Intelligence (AI)-driven framework specifically designed for carbon emission prediction in air transport to contribute to the development of sustainable smart infrastructure. The proposed hybrid model integrates XGBoost with ACGRIME, a novel metaheuristic optimization algorithm enhanced with chaos theory, adaptive weighting, and Gaussian mutation mechanisms to overcome limitations in traditional hyperparameter tuning approaches. The framework demonstrates exceptional performance on Congress on Evolutionary Computation (CEC) 2020 benchmark functions, outperforming conventional optimization algorithms in accuracy and robustness. When applied to real-world flight data within a smart transportation monitoring, ACGRIME-XGBoost achieves a 94% R2 score for CO2 emission prediction, significantly surpassing other optimized machine learning models. This research bridges the gap between advanced AI optimization techniques and sustainable transportation infrastructure, offering a scalable decision-support system that can be integrated with IoT sensor networks and mobility platforms in the future. The results demonstrate how metaheuristic-assisted machine learning can enhance environmental monitoring capabilities in smart transportation ecosystems, supporting data-driven policy-making for climate-resilient infrastructure and sustainable aviation management within the broader context. Also, the research contributes to sustainable aviation by enabling high-fidelity CO2 prediction models that can inform policy-making and be integrated into digital monitoring tools for future smart transport infrastructures. Full article
(This article belongs to the Special Issue Artificial Intelligence and IoT for Sustainable Transportation and Smart Infrastructure)
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