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Artificial Intelligence in Sustainable Transportation
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Artificial Intelligence in Sustainable Transportation

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

Deadline for manuscript submissions: 10 November 2025 | Viewed by 2956

Special Issue Editors

Prof. Dr. Ye Li

E-Mail Website
Guest Editor
Key Laboratory of Road and Traffic Engineering, Ministry of Education & College of Transportation Engineering, Tongji University, Shanghai 201804, China
Interests: artificial intelligence; low-carbon transportation
Special Issues, Collections and Topics in MDPI journals
Dr. Dominique Gruyer

E-Mail Website
Guest Editor
1. COSYS Department for Automated Vehicles Researches, Université Gustave Eiffel, 25 Allée des Marronnier, 78000 Versailles, France
2. PICS-L Lab, COSYS Department, Université Gustave Eiffel, 25 Allée des Marronnier, 78000 Versailles, France
3. The International Associated Lab ICCAM (France-Australia), Université Gustave Eiffel, 25 Allée des Marronnier, 78000 Versailles, France
Interests: automated driving; multisensor data fusion; cooperative systems; environment perception; extended perception; sensors simulation for ADAS prototyping
Special Issues, Collections and Topics in MDPI journals
Dr. Meiting Tu

E-Mail Website
Guest Editor
Key Laboratory of Road and Traffic Engineering, Ministry of Education & College of Transportation Engineering, Tongji University, Shanghai 201804, China
Interests: data-driven optimization of traffic management and control; shared mobility; green travel
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has emerged as a transformative technology, poised to revolutionize the transportation sector via optimizing systems, reducing emissions, and enhancing the overall travel experience. AI-powered solutions hold immense potential to revolutionize the landscape of sustainable transportation, thus enabling innovative strategies and technologies that can drive the transition towards a greener, more efficient, and environmentally responsible mobility ecosystem. This Special Issue aims to explore the development and implementation of AI-driven solutions to reduce environmental impacts, improve traffic efficiency, and enhance the overall sustainability of transportation systems.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  1. AI and Big data applications in the sustainable transportation.
  2. AI applications for traffic management and optimization
  3. AI-powered optimization of the shared mobility service.
  4. AI applications in freight transportation.
  5. AI-based solutions for multi-modal transportation integration and optimization.
  6. AI-based optimization of new energy vehicle operations and charging infrastructure.
  7. AI-assisted pollutant emissions reduction strategies for transportation systems.
  8. AI-based perception from infrastructure
  9. Cooperative energy management and optimization for new mobility means.
  10. AI-based traffic accident causal analysis.
  11. Connected and autonomous vehicles for green transportation  

We look forward to receiving your contributions. 

Prof. Dr. Ye Li
Dr. Dominique Gruyer
Dr. Meiting Tu
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

  • artificial intelligence
  • sustainable transportation
  • shared mobility
  • electric vehicles
  • smart mobility

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Published Papers (2 papers)

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Research

23 pages, 41884 KiB  
Article
Analysis of Factors Affecting Electric Vehicle Range Estimation: A Case Study of the Eskisehir Osmangazi University Campus
by Ahmet Alperen Polat, Sinem Bozkurt Keser, İnci Sarıçiçek and Ahmet Yazıcı
Sustainability 2025, 17(8), 3488; https://doi.org/10.3390/su17083488 - 14 Apr 2025
Viewed by 483
Abstract
In recent years, electric vehicles have become increasingly widespread, both in the logistics sector and in personal use. This increase, together with factors such as environmental concerns and government incentives, has brought energy consumption and range estimation issues to the forefront. In this [...] Read more.
In recent years, electric vehicles have become increasingly widespread, both in the logistics sector and in personal use. This increase, together with factors such as environmental concerns and government incentives, has brought energy consumption and range estimation issues to the forefront. In this study, the energy consumption of an electric cargo vehicle under different speed and load conditions is examined with an experimental and data-driven approach, and then used for range estimation. The raw data collected from the vehicle on the selected ~2 km route in Eskisehir Osmangazi University campus are combined into per-second samples with time synchronization and data cleaning. The route is divided into average of 150 m segments, and variables such as slope, energy consumption, and acceleration are calculated for each segment. Then, the data are used to train various machine learning models, such as Extra Trees, CatBoost, LightGBM, Voting Regressor, and XGBoost, and their performances regarding energy consumption-based range estimation are compared. The findings show that driving dynamics such as high speed and sudden acceleration, as well as road slope and load conditions, significantly shape the energy consumption and thus the remaining range. In particular, Extra Trees outperforms other machine learning models in terms of metrics such as R2, RMSE and, MAE, with a reasonable computational time. The results provide applicable guidance in areas such as route optimization, smart battery management, and charging infrastructure to reduce range anxiety and increase the operational efficiency of electric vehicles. Full article
(This article belongs to the Special Issue Artificial Intelligence in Sustainable Transportation)
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18 pages, 5847 KiB  
Article
Nonlinear and Threshold Effects of the Built Environment on Dockless Bike-Sharing
by Ming Chen, Ting Wang, Zongshi Liu, Ye Li and Meiting Tu
Sustainability 2024, 16(17), 7690; https://doi.org/10.3390/su16177690 - 4 Sep 2024
Cited by 3 | Viewed by 1604
Abstract
Dockless bike-sharing mobility brings considerable benefits to building low-carbon transportation. However, the operators often rush to seize the market and regulate the services without a good knowledge of this new mobility option, which results in unreasonable layout and management of shared bicycles. Therefore, [...] Read more.
Dockless bike-sharing mobility brings considerable benefits to building low-carbon transportation. However, the operators often rush to seize the market and regulate the services without a good knowledge of this new mobility option, which results in unreasonable layout and management of shared bicycles. Therefore, it is meaningful to explore the relationship between the built environment and bike-sharing ridership. This study proposes a novel framework integrated with the extreme gradient boosting tree model to evaluate the impacts and threshold effects of the built environment on the origin–destination bike-sharing ridership. The results show that most built environment features have strong nonlinear effects on the bike-sharing ridership. The bus density, the industrial ratio, the local population density, and the subway density are the key explanatory variables impacting the bike-sharing ridership. The threshold effects of the built environment are explored based on partial dependence plots, which could improve the bike-sharing system and provide policy implications for green travel and sustainable transportation. Full article
(This article belongs to the Special Issue Artificial Intelligence in Sustainable Transportation)
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