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Vehicles
Special Issues
Novel Solutions for Transportation Safety
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Novel Solutions for Transportation Safety

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 1038

Special Issue Editors

Dr. Tai-Jin Song

E-Mail Website
Guest Editor
Department of Urban Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea
Interests: traffic safety; big data analytics; digital infrastructure for traffic accident investigation
Dr. Yao Cheng

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Maryland, College Park, MD 20742, USA
Interests: arterial safety; traffic operation; signal optimization

Special Issue Information

Dear Colleagues,

In an ever-evolving world in which transportation is the backbone of economic growth and societal connectivity, it is crucial that safety is ensured. This Special Issue aims to present cutting-edge research, innovative technologies, and forward-thinking strategies that enhance the safety of transportation. This journal serves as a platform for experts, researchers, and practitioners to share their insights and findings on (1) accident prevention, (2) the management of traffic incidents, (3) emergency response, (4) smart infrastructure for traffic safety, (5) autonomous vehicle systems, (6) AI systems, and (7) data-driven approaches to enhancing safety. Papers that address other topics related to traffic safety are also welcome. By exploring these novel solutions, this Special Issue aims to foster a safer, more reliable transportation network, ultimately contributing to the well-being of communities worldwide.

Dr. Tai-Jin Song
Dr. Yao Cheng
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. 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 safety
  • autonomous vehicle
  • data-driven approach
  • incident management

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

Published Papers (2 papers)

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Research

27 pages, 7093 KiB  
Article
Integration of Visible Light Communication, Artificial Intelligence, and Rerouting Strategies for Enhanced Urban Traffic Management
by Manuela Vieira, Gonçalo Galvão, Manuel A. Vieira, Mário Véstias, Pedro Vieira and Paula Louro
Vehicles 2024, 6(4), 2106-2132; https://doi.org/10.3390/vehicles6040103 - 11 Dec 2024
Viewed by 405
Abstract
This study combines Visible Light Communication (VLC) and Artificial Intelligence (AI) to enhance traffic signal control, reduce congestion, and improve safety, through real-time monitoring and dynamic traffic management. Leveraging VLC technology, the system uses existing road infrastructure to transmit live data on vehicle [...] Read more.
This study combines Visible Light Communication (VLC) and Artificial Intelligence (AI) to enhance traffic signal control, reduce congestion, and improve safety, through real-time monitoring and dynamic traffic management. Leveraging VLC technology, the system uses existing road infrastructure to transmit live data on vehicle and pedestrian positions, speeds, and queues. AI agents, employing Deep Reinforcement Learning (DRL), process this data to manage traffic flows dynamically, applying anti-bottleneck and rerouting techniques to balance pedestrian and vehicle waiting times. A centralized global agent coordinates the local agents controlling each intersection, enabling indirect communication and data sharing to train a unified DRL model. This model makes real-time adjustments to traffic light phases, utilizing a queue/request/response system for adaptive intersection management. Tested using simulations and real-world trials involving standard and rerouting scenarios, the approach demonstrates significantly better performance in regard to the rerouting configuration, reducing congestion and enhancing traffic flow and pedestrian safety. Scalable and adaptable to various intersection types, including four-way, T-intersections, and roundabouts, the system’s efficacy is validated using the SUMO urban mobility simulator, resulting in notable reductions to travel and waiting times for both vehicles and pedestrians. Full article
(This article belongs to the Special Issue Novel Solutions for Transportation Safety)
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13 pages, 10679 KiB  
Article
Work-in-Progress Report: Intelligent Traffic Road Weather and Safety Services for Heavy Vehicles
by Timo Sukuvaara, Kari Mäenpää, Hannu Honkanen, Ari Pikkarainen, Marjo Hippi and Virve Karsisto
Vehicles 2024, 6(4), 2031-2043; https://doi.org/10.3390/vehicles6040100 - 28 Nov 2024
Viewed by 393
Abstract
Accidents involving heavy road vehicles are often destructive, causing operational losses, human casualties, infrastructure losses, and negative environmental impacts. The risk is especially high in wintertime traffic. The Eureka Xecs SafeTrucks project (Heavy traffic safety improvements by advanced dynamics and road weather services) [...] Read more.
Accidents involving heavy road vehicles are often destructive, causing operational losses, human casualties, infrastructure losses, and negative environmental impacts. The risk is especially high in wintertime traffic. The Eureka Xecs SafeTrucks project (Heavy traffic safety improvements by advanced dynamics and road weather services) develops real-time vehicle-specific weather and safety services tailored to each vehicle, based on the vehicle’s own sensor-based observations combined with data from weather service systems and an analysis of the vehicle’s own dynamics. The services will also be analyzed by Digital Twin modeling in Hardware-in-the-Loop (HIL) and Driver-In-the-Loop (DIL) scenarios, in order to evaluate and refine them in a controlled environment. This paper focuses on operative fleet piloting, while the Digital Twin approach will be presented in future work. The pilot services are ultimately tested in a pilot system within operative heavy traffic. This paper presents the concept and architecture of the platform, with preliminary results of pilot services operation, alternative communication analysis, and system evaluation. Full article
(This article belongs to the Special Issue Novel Solutions for Transportation Safety)
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