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Emerging Transportation Safety and Operations: Practical Perspectives, 2nd Edition
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Emerging Transportation Safety and Operations: Practical Perspectives, 2nd Edition

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

Deadline for manuscript submissions: 20 June 2025 | Viewed by 6956

Special Issue Editors

Prof. Dr. Deogratias Eustace

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering and Engineering Mechanics, University of Dayton, Dayton, OH, USA
Interests: highway safety; traffic operations; emerging mobility services; travel demand modeling; ITS applications; CAV/AV impacts on traffic safety; non-motorized transportation; statistical applications in transportation engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Bhaven Naik

E-Mail Website
Guest Editor
Department of Civil Engineering, Ohio University, Athens, OH, USA
Interests: traffic microsimulation modelling; highway safety and human factors research; traffic operations; signal system design and optimization; applications of ITS; geometric design; CV/AV technologies; statistical modeling and analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Heng Wei

E-Mail Website
Guest Editor
Department of Civil and Architectural Engineering and Construction Management, University of Cincinnati, Cincinnati, OH, USA
Interests: ITS; CAV impacts on transportation control system and infrastructure design; safety operations and management as well as environment; AI and advanced computing and communication technologies in transportation infrastructure systems; GIS application; vehicle routing modeling and optimization, advanced technologies in highway safety
Special Issues, Collections and Topics in MDPI journals
Dr. Parth Bhavsar

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, The Southern Polytechnic College of Engineering and Engineering Technology (SPCEET), Kennesaw State University, Marietta, GA 30060, USA
Interests: transportation data analytics; intelligent transportation system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Worldwide, it is estimated that traffic-related crashes (accidents) cause about 1.3 million deaths per year, with an additional 20–50 million people sustaining various types of injuries. Therefore, road safety is a public health issue. For many years, traffic safety professionals and researchers have believed that highway traffic-related deaths and injuries are preventable. Traffic engineers believe that transportation automation technologies such as advanced driver assistance systems, automated driving vehicles, connected vehicles, and autonomous vehicles have the potential to reduce crashes, prevent injuries, save lives, and improve traffic operations. In recent years, concerted efforts have been made to improve road safety worldwide. One of the major recognized efforts is a global multi-country effort known as Vision Zero, which was started in Sweden and has now spread all over the world. This global movement aims to use road safety systemic approach measures to end traffic-related fatalities and serious injuries. 

For this Special Issue of Vehicles entitled “Emerging Transportation Safety and Operations: Practical Perspectives,” we are seeking original contributions within this research area. Topics include but are not limited to applications of safety methods along with emerging technologies, the evaluation of traffic studies, before–after studies of safety countermeasures, operation-based safety and other impact studies, emerging trends in traffic safety and operations, surrogate measures, and applications of data-driven safety and operation methods with CAV-generated data, third-party data or other synergized data sources.

The publications in the first edition, which we believe may be of interest to you, can be found here: https://www.mdpi.com/journal/vehicles/special_issues/VP5V2662T4.

Prof. Dr. Deogratias Eustace
Dr. Bhaven Naik
Prof. Dr. Heng Wei
Dr. Parth Bhavsar
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

  • traffic safety
  • surrogate measures
  • injury severity
  • crash severity
  • connected/automated vehicle safety
  • safety methods
  • intelligent transportation systems

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

Published Papers (5 papers)

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Research

19 pages, 3338 KiB  
Article
Comparison of Machine Learning Models to Predict Nighttime Crash Severity: A Case Study in Tyler, Texas, USA
by Raja Daoud, Matthew Vechione, Okan Gurbuz, Prabha Sundaravadivel and Chi Tian
Vehicles 2025, 7(1), 20; https://doi.org/10.3390/vehicles7010020 - 18 Feb 2025
Viewed by 476
Abstract
Driving at night is riskier in terms of crash involvement than it is during the day. Fortunately, it is clearly established that illumination on roadways can reduce the number and severity of nighttime crashes. However, state and municipal departments of transportation (DOTs) lack [...] Read more.
Driving at night is riskier in terms of crash involvement than it is during the day. Fortunately, it is clearly established that illumination on roadways can reduce the number and severity of nighttime crashes. However, state and municipal departments of transportation (DOTs) lack the available illumination data. Therefore, the objective of this research is threefold, as follows: (i) to develop machine learning models that use readily available roadway characteristic data to predict the severity of nighttime crashes; (ii) determine the effect that illumination has on crash severity; and (iii) develop a tool to assist DOT decision makers in collecting illumination data. To accomplish this objective, we have extracted data from the Texas Department of Transportation (TxDOT) Crash Record Information System (CRIS) database, which was then further split into a training and a test dataset. Then, seven machine learning techniques, namely binary logistic regression, k-nearest neighbors, naïve Bayes, random forest, artificial neural network, Extreme Gradient Boosting (XGBoost), and a Long Short-Term Memory (LSTM) model, were all applied to the unseen test data. The random forest model produced the most promising results by predicting severe crashes with 97.6% accuracy. In addition, we conducted a pilot study to test the collection of illumination data using a light meter. In the future, we aim to complete the development of a smartphone application, which can be used in conjunction with the random forest model presented in this paper, to collect crowdsourced illumination data and predict nighttime crash hotspots. This may assist DOT decision makers to prioritize funding for illumination at the hot spots. Full article
(This article belongs to the Special Issue Emerging Transportation Safety and Operations: Practical Perspectives, 2nd Edition)
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17 pages, 2590 KiB  
Article
Analyzing Crash Severity: Human Injury Severity Prediction Method Based on Transformer Model
by Yalan Jiang, Xianguo Qu, Weiwei Zhang, Wenfeng Guo, Jiejie Xu, Wangpengfei Yu and Yang Chen
Vehicles 2025, 7(1), 5; https://doi.org/10.3390/vehicles7010005 - 15 Jan 2025
Viewed by 1890
Abstract
Traffic accident-related injuries and fatalities are a serious global public health and social development challenge. The accurate prediction of crash severity improves road safety and reduces casualties, as well as serving road managers and policy makers. Prediction models need to learn and analyze [...] Read more.
Traffic accident-related injuries and fatalities are a serious global public health and social development challenge. The accurate prediction of crash severity improves road safety and reduces casualties, as well as serving road managers and policy makers. Prediction models need to learn and analyze the various characteristic factors of traffic accidents and capture from them the inherent complex relationship between accident characteristics and the severity of traffic accidents. However, most accident prediction studies lack analytical predictions of injury severity, and predictive models rely on the content and quality of accident datasets. To increase the robustness and accuracy of prediction models, this paper leverages a Transformer-based architecture for the severity prediction of traffic collisions from human injury severity. This framework learns both text and sequence data from accident datasets. After comparative analysis, the framework can achieve the prediction of human injury severity under different data categories and show good prediction performance at low injury severity levels using only textual data or sequence data. Full article
(This article belongs to the Special Issue Emerging Transportation Safety and Operations: Practical Perspectives, 2nd Edition)
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25 pages, 3488 KiB  
Article
Emerging Decision-Making for Transportation Safety: Collaborative Agent Performance Analysis
by Jack Maguire-Day, Saba Al-Rubaye, Anirudh Warrier, Muhammet A. Sen, Huw Whitworth and Mohammad Samie
Vehicles 2025, 7(1), 4; https://doi.org/10.3390/vehicles7010004 - 15 Jan 2025
Viewed by 1509
Abstract
This paper addresses the challenge of improving decision-making capabilities and safety in autonomous vehicles (AVs) using Agent-Based Modelling (ABM). The study evaluates ABM’s effect on Advanced Driver Assistance Systems (ADASs) in challenging driving situations, like lane merging, by incorporating it into a simulation [...] Read more.
This paper addresses the challenge of improving decision-making capabilities and safety in autonomous vehicles (AVs) using Agent-Based Modelling (ABM). The study evaluates ABM’s effect on Advanced Driver Assistance Systems (ADASs) in challenging driving situations, like lane merging, by incorporating it into a simulation framework designed for autonomous vehicles. Identifying emergent behaviours that enhance safety and efficiency, verifying the efficacy of ABM in AV decision-making, and investigating the function of hardware acceleration to enable practical application in ADASs are some of the major achievements. According to the simulation results, ABM can greatly improve AV performance, providing a practical and scalable means of enhancing safety in future transportation systems. Full article
(This article belongs to the Special Issue Emerging Transportation Safety and Operations: Practical Perspectives, 2nd Edition)
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19 pages, 4236 KiB  
Article
Implementation of Re-Simulation-Based Integrated Analysis System to Evaluate and Improve Autonomous Driving Algorithms
by Soobin Jeon, Junehong Park and Dongmahn Seo
Vehicles 2024, 6(4), 2209-2227; https://doi.org/10.3390/vehicles6040108 - 22 Dec 2024
Viewed by 1131
Abstract
Autonomous driving technology requires rigorous testing and validation of perception, decision-making, and control algorithms to ensure safety and reliability. Although existing simulators and testing tools play critical roles in algorithm evaluation, they struggle to satisfy the demands of complex, real-time systems. This study [...] Read more.
Autonomous driving technology requires rigorous testing and validation of perception, decision-making, and control algorithms to ensure safety and reliability. Although existing simulators and testing tools play critical roles in algorithm evaluation, they struggle to satisfy the demands of complex, real-time systems. This study proposes a re-simulation-based integrated analysis system designed to overcome these challenges by providing advanced visualization, algorithm-testing, re-simulation, and data-handling capabilities. The proposed system features a comprehensive visualization module for real-time analysis of diverse sensor data and ego vehicle information, offering intuitive insights to researchers. Additionally, it includes a flexible algorithm-testing framework that abstracts simulator-specific dependencies, enabling seamless integration and evaluation of algorithms in various scenarios. The system also introduces robust re-simulation capabilities, enhancing algorithm validation using iterative testing based on real-world or simulated sensor data. To address the computational demands of high-frequency sensor data, the system employs optimized data-handling mechanisms based on shared memory, thereby significantly reducing latency and improving scalability. The proposed system overcomes critical challenges faced by existing alternatives by providing a robust, efficient, and scalable solution for testing and validating autonomous-driving algorithms, ultimately accelerating the development of safe and reliable autonomous vehicles. Full article
(This article belongs to the Special Issue Emerging Transportation Safety and Operations: Practical Perspectives, 2nd Edition)
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12 pages, 1492 KiB  
Article
Are Safety Corridors Effective in Mitigating Safety? An Ohio-Based Case Study Evaluating Their Effectiveness
by Sudesh Ramesh Bhagat, Bernard Ndeogo Issifu, Devon Destocki, Bhaven Naik and Deogratias Eustace
Vehicles 2024, 6(4), 1963-1974; https://doi.org/10.3390/vehicles6040096 - 24 Nov 2024
Viewed by 888
Abstract
Distracted driving remains a major concern on highways, with it contributing to severe and fatal crashes, particularly on high-speed routes, prompting numerous states to implement targeted initiatives aimed at combating traffic violations that significantly contribute to fatal and injury-inducing crashes. Among these initiatives [...] Read more.
Distracted driving remains a major concern on highways, with it contributing to severe and fatal crashes, particularly on high-speed routes, prompting numerous states to implement targeted initiatives aimed at combating traffic violations that significantly contribute to fatal and injury-inducing crashes. Among these initiatives is the highway safety corridor program, a collaborative endeavor between the state departments of transportation and law enforcement agencies. Highway safety corridors employ a combination of engineering interventions and heightened law enforcement presence to address risky driver behavior and mitigate the occurrence of crashes. Despite the longstanding existence of safety corridors, research on their effectiveness remains relatively limited, with existing studies indicating only moderate success rates. This study is dedicated to evaluating the effectiveness of ten highway safety corridors in Ohio, where the state recently launched its inaugural highway safety corridor program targeting distracted driving. Utilizing 2023 crash data, this Empirical Bayes’ before-and-after study seeks to gauge the impact of these safety corridors on enhancing roadway transportation safety. Upon assessing all crash types within Ohio’s distracted driving safety corridors that provided sufficient data for a before–after study, it was determined that the adoption of safety corridors generally led to a reduction in crashes ranging from 2% to 49%. The significance and magnitude of crash reduction may vary if specific crash types or severity levels are considered. Full article
(This article belongs to the Special Issue Emerging Transportation Safety and Operations: Practical Perspectives, 2nd Edition)
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