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Vehicles
Special Issues
Vehicle Systems and Road Infrastructure Integration for Smarter Transportation Systems
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Vehicle Systems and Road Infrastructure Integration for Smarter Transportation Systems

A special issue of Vehicles (ISSN 2624-8921).

Deadline for manuscript submissions: 31 May 2026 | Viewed by 1192

Special Issue Editors

Prof. Dr. Anna Grana

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Guest Editor
Department of Engineering, University of Palermo, 90128 Palermo, Italy
Interests: sustainable geometric road and intersection design; road safety and modeling; microscopic traffic simulation; traffic management
Special Issues, Collections and Topics in MDPI journals
Dr. Elżbieta Macioszek

E-Mail Website
Guest Editor
Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, Krasińskiego 8 Street, 40-019 Katowice, Poland
Interests: road traffic engineering; road and intersections capacity analysis; measurements; traffic modeling; research and traffic flow analysis; transport infrastructure; functional analysis; transport systems and processes modeling; transportation engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Maria Luisa Tumminello

E-Mail Website
Guest Editor
Department of Engineering, University of Palermo, 90133 Palermo, Italy
Interests: performance analysis of road transport networks; road traffic micro-simulation models; connected and automated driving technologies; sustainability and environmental impact of road facilities; analysis of road safety; surrogate measures of safety; road design solutions; traffic flows management; traffic calming measures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to explore the integration of vehicle systems and road infrastructure as the foundation for smarter, more efficient transportation networks. With advancements in intelligent transportation systems, connected vehicle technologies, and infrastructure-to-vehicle communication, it is now possible to develop adaptive mobility solutions for road spaces.

The focus of this Issue is to examine how innovative vehicle technologies synergize with modern road infrastructure to enhance safety, reduce congestion, and improve operational efficiency through energy harvesting and energy-efficient solutions. By utilizing ambient energy sources and implementing energy-efficient strategies, the research aims to minimize waste, lower costs, and improve overall operational effectiveness.

Research within this theme spans a broad range of technological areas, including vehicle-to-infrastructure (V2I) communication, intelligent traffic management, sensor networks, data analytics, infrastructure resilience, as well as road maintenance and vehicle management systems. Developing new tools and analytical methods to evaluate these integrated systems is essential to advancing the state of the art and supporting real-world deployment.

Experiments and field trials play a critical role in validating theoretical models and demonstrating practical applications. Such efforts accelerate the transition of research into operational solutions that can benefit urban and rural transportation environments.

This Special Issue invites submissions that explore the technological, methodological, and practical aspects of vehicle and road infrastructure integration. Emphasis is placed on innovative road design solutions, system architectures for ensuring seamless operation between vehicles and infrastructure, and strategies for deploying smarter, more efficient transportation systems throughout their life cycle.

Articles, reviews, and other types of contributions are welcome, in accordance with the editorial guidelines.

Prof. Dr. Anna Grana
Prof. Dr. Elżbieta Macioszek
Dr. Maria Luisa Tumminello
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. Vehicles is an international peer-reviewed open access quarterly 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 1600 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

  • vehicle systems
  • road infrastructure
  • life-cycle analysis
  • smart mobility
  • energy harvesting and energy-efficient road design solutions
  • smart city planning
  • adaptive traffic management
  • smart and connected transportation systems
  • internet of vehicles (IoV)
  • digital solutions for integrated vehicle-road projects

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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

27 pages, 6182 KB  
Article
Graph-Based Deep Learning and Multi-Source Data to Provide Safety-Actionable Insights for Rural Traffic Management
by Taimoor Ali Khan and Yaqin Qin
Vehicles 2025, 7(4), 151; https://doi.org/10.3390/vehicles7040151 - 5 Dec 2025
Viewed by 207
Abstract
This study confronts the significant challenges inherent in Traffic State Estimation (TSE) for rural arterial networks, where sparse sensor coverage and complex, dynamic traffic flows complicate effective management and safety assurance. Traditional TSE methodologies, often dependent on single-source data streams, fail to accurately [...] Read more.
This study confronts the significant challenges inherent in Traffic State Estimation (TSE) for rural arterial networks, where sparse sensor coverage and complex, dynamic traffic flows complicate effective management and safety assurance. Traditional TSE methodologies, often dependent on single-source data streams, fail to accurately model the intricate spatiotemporal dependencies present in such environments. This fundamental limitation precipitates critical safety hazards, including pervasive over speeding and dangerous queue spillback phenomena at intersections. To address these deficiencies, we introduce a novel hybrid intelligence framework that synergistically combines a Graph Attention Temporal Convolutional Network (GAT-TCN) with advanced Kalman Filter variants, specifically the Extended, Unscented, and Sliding Window Kalman Filters. The GAT-TCN component is engineered to excel at learning complex, non-linear correlations across both space and time through multi-source data fusion. Empirical validation conducted on a real-world rural toll corridor demonstrates that our proposed model achieves a statistically significant superiority over conventional benchmarks, as rigorously quantified by substantial reductions in both Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Beyond mere predictive accuracy, the framework delivers transformative safety enhancements by facilitating the proactive identification of hazardous events, enabling earlier detection of over speeding and queue spillback compared to existing methods. Consequently, this research provides a scalable and robust framework for proactive rural traffic management, fundamentally shifting the paradigm from achieving incremental predictive improvements to generating decisive, safety-actionable insights for infrastructure operators. Full article
(This article belongs to the Special Issue Vehicle Systems and Road Infrastructure Integration for Smarter Transportation Systems)
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24 pages, 19334 KB  
Article
Enhancing Highway Emergency Lane Control via Koopman Graph Mamba: An Interpretable Dynamic Decision Model
by Hao Li, Zi Wang, Haoran Zhang, Wenning Hao and Li Xiang
Vehicles 2025, 7(4), 129; https://doi.org/10.3390/vehicles7040129 - 10 Nov 2025
Viewed by 677
Abstract
Intelligent Transportation Systems (ITS) play a pivotal role in addressing traffic congestion, inefficiency, and safety concerns. Emergency lane control on highways is a critical ITS component, yet existing strategies often lack flexibility, theoretical rigor, and the ability to handle dynamic spatiotemporal interactions under [...] Read more.
Intelligent Transportation Systems (ITS) play a pivotal role in addressing traffic congestion, inefficiency, and safety concerns. Emergency lane control on highways is a critical ITS component, yet existing strategies often lack flexibility, theoretical rigor, and the ability to handle dynamic spatiotemporal interactions under uncertain data. To address these limitations, this paper introduces Koopman Graph Mamba (KGM), an innovative framework integrating the Koopman operator with a graph-based state space model for dynamic emergency lane control. KGM leverages multimodal traffic data to predict spatiotemporal patterns, facilitating real-time decisions. An interpretable decision module based on fuzzy neural networks ensures context-sensitive decisions. Evaluated on a real-world dataset from the Changshen Expressway (Nanjing-Changzhou section) and public datasets including NGSIM, PeMS04, and PeMS08, KGM demonstrates superior performance with linear computational complexity, underscoring its potential for large-scale, real-time applications. Full article
(This article belongs to the Special Issue Vehicle Systems and Road Infrastructure Integration for Smarter Transportation Systems)
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