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Vehicles
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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 5397

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 policies can be found here.

Published Papers (7 papers)

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Research

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30 pages, 1818 KiB  
Article
Pooled Rideshare in the U.S.: An Exploratory Study of User Preferences
by Rakesh Gangadharaiah, Johnell Brooks, Lisa Boor, Kristin Kolodge, Haotian Su and Yunyi Jia
Vehicles 2025, 7(2), 44; https://doi.org/10.3390/vehicles7020044 - 9 May 2025
Viewed by 232
Abstract
Pooled ridesharing offers on-demand, one-way, cost-effective transportation for passengers traveling in similar directions via a shared vehicle ride with others they do not know. Despite its potential benefits, the adoption of pooled rideshare remains low in the United States. This exploratory study aims [...] Read more.
Pooled ridesharing offers on-demand, one-way, cost-effective transportation for passengers traveling in similar directions via a shared vehicle ride with others they do not know. Despite its potential benefits, the adoption of pooled rideshare remains low in the United States. This exploratory study aims to evaluate potential service improvements and features that may increase users’ willingness to adopt the service. The study analyzed transportation behaviors, rideshare preferences, and willingness to adopt pooled rideshare services among 8296 U.S. participants in 2025, building on findings from a 2021 nationwide survey of 5385 U.S. participants. The study incorporated 77 actionable items developed from the results of the 2021 survey to assess whether addressing specific user-generated topics such as safety, reliability, convenience, and privacy can improve pooled rideshare use. A side-by-side comparison of the 2021 and 2025 data revealed shifts in transportation behavior, with personal rideshare usage increasing from 22% to 28%, public transportation from 21% to 27%, and pooled rideshare from 6% to 8%, while personal vehicle (79%) use remained dominant. Participants rated features such as driver verification (94%), vehicle information (93%), peak time reliability (93%), and saving time and money (92–93%) as most important for improving rideshare services. A pre-to-post analysis of willingness to use pooled rideshare utilizing the actionable items as per respondents’ preferences showed improvement: “definitely will” increased from 15.9% to 20.1% and “probably will” rose from 35.6% to 47.7%. These results suggest that well-targeted service improvements may meaningfully enhance pooled rideshare acceptance. This study offers practical guidance for Transportation Network Companies (TNCs) and policymakers aiming to improve pooled rideshare as well as potential future research opportunities. Full article
(This article belongs to the Special Issue Novel Solutions for Transportation Safety)
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27 pages, 6543 KiB  
Article
Driver Injury Prediction and Factor Analysis in Passenger Vehicle-to-Passenger Vehicle Collision Accidents Using Explainable Machine Learning
by Peng Liu, Weiwei Zhang, Xuncheng Wu, Wenfeng Guo and Wangpengfei Yu
Vehicles 2025, 7(2), 42; https://doi.org/10.3390/vehicles7020042 - 3 May 2025
Viewed by 177
Abstract
Vehicle accidents, particularly PV-PV collisions, result in significant property damage and driver injuries, causing substantial economic losses and health risks. Most existing studies focus on macro-level predictions, such as accident frequency, but lack detailed collision-level analysis, which limits the precision of severity prediction. [...] Read more.
Vehicle accidents, particularly PV-PV collisions, result in significant property damage and driver injuries, causing substantial economic losses and health risks. Most existing studies focus on macro-level predictions, such as accident frequency, but lack detailed collision-level analysis, which limits the precision of severity prediction. This study investigates various accident-related factors, including environmental conditions, vehicle attributes, driver characteristics, pre-crash scenarios, and collision dynamics. Data from NHTSA’s CRSS and FARS datasets were integrated and balanced using random over-sampling and under-sampling techniques to address severity-level data imbalances. The mRMR algorithm was employed for feature selection to minimize redundancy and identify key features. Five advanced machine learning models were evaluated for severity prediction, with XGBoost achieving the best performance: 84.9% accuracy, 84.85% precision, 84.90% recall, and an F1-score of 84.87%. SHAP analysis was utilized to interpret the model and conduct a comprehensive analysis of accident features, including their importance, dependencies, and combined effects on severity prediction. This study achieved high accuracy in predicting accident severity across all levels in PV-PV collisions. Moreover, by integrating the SHAP model interpretation method, we conducted detailed feature analysis at global, local, and individual case levels, thereby filling the gap in PV-PV accident severity prediction and feature analysis. Full article
(This article belongs to the Special Issue Novel Solutions for Transportation Safety)
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35 pages, 15234 KiB  
Article
Assessment of the Potential of a Front Brake Light to Prevent Crashes and Mitigate the Consequences of Crashes at Junctions
by Ernst Tomasch, Bernhard Kirschbaum and Wolfgang Schubert
Vehicles 2025, 7(2), 40; https://doi.org/10.3390/vehicles7020040 - 29 Apr 2025
Viewed by 151
Abstract
Safe vehicles are an important pillar in reducing the number of accidents or mitigating the consequences of a collision. Although the number of autonomous safety systems in vehicles is increasing, retrofitted systems could also help reduce road accidents. A new retrofit assistance system [...] Read more.
Safe vehicles are an important pillar in reducing the number of accidents or mitigating the consequences of a collision. Although the number of autonomous safety systems in vehicles is increasing, retrofitted systems could also help reduce road accidents. A new retrofit assistance system called Front Brake Light (FBL) helps the driver to assess the intentions of other road users. This system is mounted at the front of the vehicle and works similarly to the rear brake lights. The objective of this study is to evaluate the safety performance of an FBL in real accidents at junctions. Depending on the type of accident, between 7.5% and 17.0% of the accidents analysed can be prevented. A further 9.0% to 25.5% could be positively influenced by the FBL; i.e., the collision speed could be reduced. If the FBLs were visible to the driver of the priority vehicle, the number of potentially avoidable accidents would increase to a magnitude of 11.5% to 26.2%. The range of accidents in which the consequences can be reduced increases to between 13.8% and 39.2%. Full article
(This article belongs to the Special Issue Novel Solutions for Transportation Safety)
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26 pages, 3977 KiB  
Article
Enhancing Traffic Accident Severity Prediction: Feature Identification Using Explainable AI
by Jamal Alotaibi
Vehicles 2025, 7(2), 38; https://doi.org/10.3390/vehicles7020038 - 28 Apr 2025
Viewed by 620
Abstract
The latest developments in Advanced Driver Assistance Systems (ADAS) have greatly enhanced the comfort and safety of drivers. These technologies can identify driver abnormalities like fatigue, inattention, and impairment, which are essential for averting collisions. One of the important aspects of this technology [...] Read more.
The latest developments in Advanced Driver Assistance Systems (ADAS) have greatly enhanced the comfort and safety of drivers. These technologies can identify driver abnormalities like fatigue, inattention, and impairment, which are essential for averting collisions. One of the important aspects of this technology is automated traffic accident detection and prediction, which may help in saving precious human lives. This study aims to explore critical features related to traffic accident detection and prevention. A public US traffic accident dataset was used for the aforementioned task, where various machine learning (ML) models were applied to predict traffic accidents. These ML models included Random Forest, AdaBoost, KNN, and SVM. The models were compared for their accuracies, where Random Forest was found to be the best-performing model, providing the most accurate and reliable classification of accident-related data. Owing to the black box nature of ML models, this best-fit ML model was executed with explainable AI (XAI) methods such as LIME and permutation importance to understand its decision-making for the given classification task. The unique aspect of this study is the introduction of explainable artificial intelligence which enables us to have human-interpretable awareness of how ML models operate. It provides information about the inner workings of the model and directs the improvement of feature engineering for traffic accident detection, which is more accurate and dependable. The analysis identified critical features, including sources, descriptions of weather conditions, time of day (weather timestamp, start time, end time), distance, crossing, and traffic signals, as significant predictors of the probability of an accident occurring. Future ADAS technology development is anticipated to be greatly impacted by the study’s conclusions. A model can be adjusted for different driving scenarios by identifying the most important features and comprehending their dynamics to make sure that ADAS systems are precise, reliable, and suitable for real-world circumstances. Full article
(This article belongs to the Special Issue Novel Solutions for Transportation Safety)
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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 1512
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 791
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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Review

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15 pages, 1041 KiB  
Review
Assessment of Road Vehicle Accident Approaches—A Review
by Irina Duma, Nicolae Burnete, Adrian Todoruț, Nicolae Cordoș, Cosmin-Constantin Danci and Alexandru Terec
Vehicles 2025, 7(1), 10; https://doi.org/10.3390/vehicles7010010 - 27 Jan 2025
Viewed by 1237
Abstract
Given the complexity of the crashes and the increasing interest in public policies related to the reduction in both accidents and fatalities from road crashes, the proposed review of the specialty literature may serve as a starting point for individuals interested in developing [...] Read more.
Given the complexity of the crashes and the increasing interest in public policies related to the reduction in both accidents and fatalities from road crashes, the proposed review of the specialty literature may serve as a starting point for individuals interested in developing studies related to road vehicle accidents, reconstruction methodologies, assessment of vehicles crashworthiness, as well as evaluation of occupants’ behavior in different collision scenarios. Therefore, the present paper aims to offer a comprehensive overview of the specialty literature approaches in terms of road vehicle accidents through an analysis of the reconstruction methods used in the cases of vehicle-to-vehicle or vehicle-to-object crashes, as well as ways in which the crashworthiness of road vehicles is assessed by specialized organizations or individual experts. The addressed topics were summarized from a range of European and global strategies in the field of transportation, reports, testing protocols, as well as scientific research papers published in international databases. The main purpose of the present paper is to serve as a foundational resource for researchers and practitioners seeking to contextualize their work within a global framework. Full article
(This article belongs to the Special Issue Novel Solutions for Transportation Safety)
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