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Systems
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AI Applications in Transportation and Logistics
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AI Applications in Transportation and Logistics

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Artificial Intelligence and Digital Systems Engineering".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1683

Special Issue Editors

Prof. Dr. Eugen Rosca

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Guest Editor
Transport Faculty, National University of Science and Technology Politehnica Bucharest, Splaiul Independentei, No 313, RO-060042 Bucharest, Romania
Interests: transport; logistics; agent-based modeling; computer simulation; applied statistics and data analysis; AI in transportation; transport sustainability
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Radovan Madleňák

E-Mail Website
Guest Editor
Faculty of Operation and Economics of Transport and Communications, University of Žilina, Univerzitná 8215/1, 010 26 Žilina, Slovakia
Interests: transportation planning; logistics; transport modeling; optimization methods; e-business; social networks; transport economics; human-computer interaction; IT infrastructure
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Florin Valentin Rusca

E-Mail Website
Guest Editor
Transport Faculty, National University for Science and Technology Politehnica Bucharest, Spl Independentei, No 313, RO-060042 Bucharest, Romania
Interests: transport; traffic simulation; urban planning; transport reliability; transport sustainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As transportation systems and global supply chains grow more complex, dynamic, and digitalized, artificial intelligence (AI) emerges as a powerful tool to optimize operations, predict disruptions, and enhance decision-making across strategic, tactical, and operational levels. At its core, AI represents a shift from reactive problem-solving to proactive, intelligent systems that learn, adapt, and optimize. It embodies the belief that efficiency, sustainability, and resilience in transportation and logistics are not just human challenges but can be augmented, or even redefined, by machine intelligence. AI does not merely automate tasks; it introduces a new paradigm where data becomes insight, patterns reveal opportunities, and networks self-adjust to dynamic conditions. By integrating predictive analytics with IoT and big data, these AI-driven systems create more adaptive, fast, and cost-effective transportation and logistics systems capable of meeting the demands of Industry 5.0. AI in this field reflects a deeper aspiration: to harmonize technological and human interactions with the fluidity of real-world movement. It is not just about faster deliveries or smoother traffic, it is about reshaping connectivity itself, where systems think, adapt, and evolve alongside the societies they serve. By compiling original research and review articles, this Special Issue serves as a vital resource for practitioners, planners, and researchers seeking data-driven, AI-enhanced solutions to transform transportation and logistics systems into more adaptive and future-ready ones.

 Research topics on AI Applications in Transportation and Logistics include, but are not limited to, the following:

  • System Optimization: AI-driven approaches for improving transportation and logistics systems design and operations.
  • Performance Prediction: Machine learning for forecasting system behavior under varying conditions.
  • Policy and Scenario Testing: Evaluating transport policies and conducting what-if analyses for better decision-making.
  • Traffic and Congestion Management: AI-based solutions for analyzing and mitigating traffic flow disruptions.
  • Freight and Logistics: Intelligent routing, supply chain optimization, and automation in cargo movement.
  • Risk and Safety: AI applications in disruption management, accident prevention, and system reliability.
  • Emerging Technologies: Digital twins, IoT, and agent-based modeling for smarter transportation networks.
  • Autonomous Vehicles: Assessing their influence on traffic dynamics and control strategies.
  • Sustainability: Environmental impact assessments and green logistics powered by AI.

Prof. Dr. Eugen Rosca
Prof. Dr. Radovan Madleňák
Prof. Dr. Florin Valentin Rusca
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 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. Systems is an international peer-reviewed open access monthly 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

  • AI-powered transport and traffic management
  • smart logistics optimization
  • machine learning for predictive modeling and forecasting
  • AI and autonomous vehicles
  • digital twin for supply chains
  • sustainable logistics with AI
  • AI for risks and accident prevention
  • human-AI collaboration in logistics

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

29 pages, 3434 KB  
Article
An XGBoost Approach to Identifying Hinterland Drivers of Inland Port Development
by Eugen Rosca, Cristina Oprea, Mircea Rosca, Stefan Burciu, Alina Roman and Florin Rusca
Systems 2026, 14(4), 395; https://doi.org/10.3390/systems14040395 - 3 Apr 2026
Viewed by 267
Abstract
Inland ports play a strategic role in enhancing multimodal connectivity and promoting sustainable freight transport within European corridors. However, the drivers of inland port development remain insufficiently understood, particularly with respect to nonlinear dynamics, interaction effects, and regional heterogeneity. This study investigates the [...] Read more.
Inland ports play a strategic role in enhancing multimodal connectivity and promoting sustainable freight transport within European corridors. However, the drivers of inland port development remain insufficiently understood, particularly with respect to nonlinear dynamics, interaction effects, and regional heterogeneity. This study investigates the socio-economic, infrastructural, and spatial determinants of inland port throughput using an interpretable machine learning framework. An XGBoost model is built up to estimate eighteen ports’ throughput along the Romanian Danube, over the period 2010–2024. SHAP (Shapley Additive Explanations) values are employed to quantify global importance, nonlinear marginal effects, and interaction structures. Results show that spatial accessibility and road infrastructure are the most influential drivers, while economic sectoral structure and road infrastructure exert nonlinear and scale-dependent effects. Interaction analysis reveals that inland port development is synergy-driven rather than additive, with the strongest complementarities observed between spatial accessibility, multimodal infrastructure, and sectoral structure. Additionally, Kruskal–Wallis tests on SHAP contributions indicate significant heterogeneity across port administrations, suggesting that governance and regional context modulate the realization of economic and infrastructural potential. The findings contribute to port–hinterland interaction analysis by demonstrating that inland port performance emerges from multi-scale, nonlinear, and regionally mediated dynamics. Methodologically, the study illustrates the value of interpretable machine learning for transport systems research. Policy implications emphasize coordinated multimodal investments, accessibility enhancement, and region-specific development strategies to strengthen inland waterway integration within the European transport sector. Full article
(This article belongs to the Special Issue AI Applications in Transportation and Logistics)
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22 pages, 1035 KB  
Article
Investigating User Acceptance of Autonomous Vehicles in Developing Cities Using Machine Learning: Lessons from Alexandria, Egypt
by Sherif Shokry, Ahmed Mahmoud Darwish, Hazem Mohamed Darwish, Omar Elsnossy Ibrahim, Maged Zagow, Marwa Elbany and Usama Elrawy Shahdah
Systems 2026, 14(1), 45; https://doi.org/10.3390/systems14010045 - 31 Dec 2025
Viewed by 562
Abstract
The willingness to adopt Autonomous Vehicles (AVs) represents a crucial advancement from the sustainable mobility perspective. This is progressively continuing in the developed countries. A comparable shift is expected in developing nations; however, empirical studies remain limited, especially in areas where AVs have [...] Read more.
The willingness to adopt Autonomous Vehicles (AVs) represents a crucial advancement from the sustainable mobility perspective. This is progressively continuing in the developed countries. A comparable shift is expected in developing nations; however, empirical studies remain limited, especially in areas where AVs have not yet been deployed. This study investigates the willingness to adopt AVs in a developing city where AVs have not been deployed yet. A comprehensive travel behavior questionnaire was conducted among local commuters in Alexandria, Egypt, to identify the influential variables affecting AV choice. The well-known machine learning classifier, Extreme Gradient Boosting (XGB), was employed to develop a forecasting model, which indicated a notable accuracy. The results indicated that trip cost was the most influential feature. On the other hand, there is a considerable level of mode captivity, since most travelers prefer to remain with their current mode, regardless of the effects of other variables. A significant share of travelers expressed concerns about shifting to AVs due to safety worries associated with the travel behavior of other transportation modes’ commuters. The analysis provides nuanced perspectives on the variables promoting modal shift toward the AVs, supporting future policies for smart urban mobility. Full article
(This article belongs to the Special Issue AI Applications in Transportation and Logistics)
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