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Applied Sciences
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Traffic Safety Measures and Assessment: 2nd Edition
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Traffic Safety Measures and Assessment: 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 20 September 2026 | Viewed by 3632

Special Issue Editors

Prof. Dr. Xinguo Jiang

E-Mail Website
Guest Editor
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China
Interests: safety modeling; safety behavior analysis; surrogate traffic safety analysis; traffic safety evaluation
Special Issues, Collections and Topics in MDPI journals
Dr. Konstantinos Mattas

E-Mail Website
Guest Editor
European Commission’s Joint Research Centre, Ispra, Italy
Interests: road safety; transportation; automated driving; fuzzy systems; transportation science; traffic simulation

Special Issue Information

Dear Colleagues,

A significant number of studies have been conducted in recent decades in attempt to reduce crash frequency and severity for roadway users. In order to achieve this goal, safety measures play an important role in quantifying various safety conditions/scenarios, and safety assessment is vital to evaluating the cost–benefit ratio of safety treatments.

With this Special Issue, we aim to propose innovative methods and statistical techniques to quantify traffic safety risks and evaluate the effectiveness of the newly developed safety improvements. The analytic methods included in this SI are expected to differ considerably from traditional ones.

We welcome all individuals interested in this topic to contribute to this Special Issue. I look forward to receiving your submissions.

Prof. Dr. Xinguo Jiang
Dr. Konstantinos Mattas
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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • safety statistical modeling
  • safety behavior analysis
  • surrogate traffic safety analysis
  • traffic safety evaluation

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Related Special Issue

Published Papers (5 papers)

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Research

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18 pages, 6067 KB  
Article
Examining the Non-Linear Effects of Risky Driving Behaviors on Traffic Accidents: A Case Study of Daejeon, Korea
by Songjun Yeom, Yuseok Lee and Minjun Kim
Appl. Sci. 2026, 16(10), 4628; https://doi.org/10.3390/app16104628 - 8 May 2026
Viewed by 293
Abstract
Despite extensive research on traffic safety, the complex, non-linear spatial discrepancy between risky driving and actual accidents remains a significant challenge to quantify within diverse urban contexts. This study investigates the non-linear relationship between grid-level risky driving patterns and traffic accident occurrence in [...] Read more.
Despite extensive research on traffic safety, the complex, non-linear spatial discrepancy between risky driving and actual accidents remains a significant challenge to quantify within diverse urban contexts. This study investigates the non-linear relationship between grid-level risky driving patterns and traffic accident occurrence in Daejeon, Korea, examining how these associations vary across different urban contexts. Using data collected from July 2023 to June 2024, the analysis incorporates GPS-based risky driving indicators, including rapid acceleration, deceleration, and sudden maneuvers from general passenger vehicles, thereby overcoming the limitations of previous studies reliant on commercial vehicle data. By adopting an H3-based spatial grid system, the study classifies areas into four quadrants based on median values of risky behaviors and accident counts, further categorizing them into “Matched” and “Mismatched” types to identify spatial discrepancies. Furthermore, the Explainable Artificial Intelligence (XAI) technique is employed to integrate regional variables—including population density, land use, and transport infrastructure—to uncover the key drivers of accident risks. Providing a significant methodological improvement over traditional linear models, the findings demonstrate that identical driving behaviors can yield different safety outcomes depending on local environmental interactions. Specifically, while driver behavioral factors directly explain accident frequency in matched regions, accident risks in mismatched regions are more significantly shaped by spatial environmental factors, such as green spaces and commercial land use, which override direct behavioral impacts. This study provides a robust framework for developing data-driven, region-specific traffic intervention strategies, including context-aware advanced driver assistance systems (ADAS) and spatially tailored traffic calming, to enhance urban safety. Full article
(This article belongs to the Special Issue Traffic Safety Measures and Assessment: 2nd Edition)
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14 pages, 843 KB  
Article
Modeling the Interdependence of Vehicle-Level Injury Severities of Bus–Taxi Crashes: A Random-Parameters Bivariate Probit Approach
by Jing Huang, Zheliang He, Jun Li, Qiang Zeng and Xiaofei Wang
Appl. Sci. 2026, 16(6), 2783; https://doi.org/10.3390/app16062783 - 13 Mar 2026
Viewed by 467
Abstract
Prior studies have typically analyzed the injury severity of bus or taxi passengers at the crash level or single-vehicle level, neglecting vehicle-level interdependence between them. To address the gap, this research sets out to analyze the factors contributing to the vehicle-level injury severities [...] Read more.
Prior studies have typically analyzed the injury severity of bus or taxi passengers at the crash level or single-vehicle level, neglecting vehicle-level interdependence between them. To address the gap, this research sets out to analyze the factors contributing to the vehicle-level injury severities of transit bus–taxi crashes, with consideration of their interdependence and heterogeneities. The random-parameters bivariate probit model, which can capture both unobserved heterogeneity and within-crash correlation between bus and taxi injury outcomes, was advocated for the joint analysis. In the model, the factors related to the two vehicles and their drivers, together with other factors (e.g., roadway, environment, and crash configuration), were used as the explanatory variables. A total of 3404 two-vehicle bus–taxi crash records in Hong Kong, China, from 2009 to 2019 were used for model estimation. The results indicate that taxi driver age, taxi age, crash location, and collision manner resulted in heterogeneous effects on bus injury severity, and the time of day yielded a heterogeneous effect on taxi injury severity. In addition, bus driver error and street light resulted in fixed yet moderate (less than 6%) effects on bus injury severity, while taxi driver gender, speed limit, rainfall, and collision manner resulted in fixed effects on taxi injury severity, where female drivers and front collisions significantly increased the likelihood of fatality and severe injury with their marginal effects more than 20%. Based on the findings, tailored strategies pertaining to safety education, law enforcement, vehicle safety devices, and traffic management and control were proposed to mitigate crash outcomes involving public buses and taxis. Full article
(This article belongs to the Special Issue Traffic Safety Measures and Assessment: 2nd Edition)
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27 pages, 5137 KB  
Article
Research on Anti-Underride Design of Height-Optimized Class A W-Beam Guardrail
by Xitai Feng, Jiangbi Hu and Qingxin Hu
Appl. Sci. 2025, 15(23), 12631; https://doi.org/10.3390/app152312631 - 28 Nov 2025
Viewed by 616
Abstract
As an essential highway safety facility, roadside W-beam guardrails effectively prevent errant vehicles from entering hazardous zones or causing secondary collisions by blocking and redirecting them, thereby reducing accident severity. With the rapid development of the automotive industry, the front bumper height of [...] Read more.
As an essential highway safety facility, roadside W-beam guardrails effectively prevent errant vehicles from entering hazardous zones or causing secondary collisions by blocking and redirecting them, thereby reducing accident severity. With the rapid development of the automotive industry, the front bumper height of small passenger cars generally ranges between 405 mm and 485 mm. However, the lower edge height of the current Chinese Class A W-beam guardrail is 444 mm above the ground, which leads to a high risk of “underride” during collisions, resulting in elevated occupant injury risks. To address this issue, this paper proposes an optimized guardrail structure composed of a double W-beam and a C-type beam, aiming to reduce the underride risk for small passenger cars while accommodating multi-vehicle protection needs. In this design, the double W-beam is installed at a height of 560 mm and the C-type beam at 850 mm, connected to circular posts using a regular hexagonal anti-obstruction block. The beam thickness is uniformly 3 mm, while the thickness of other components is 4 mm. To systematically evaluate the impact of material strength on both safety performance and cost, two material configurations are proposed: Scheme 1 uses Q235 carbon steel for all components; Scheme 2 reduces the thickness of the C-type beam to 2.5 mm and employs Q355 high-strength low-alloy steel, with the thickness of the connected anti-obstruction block reduced to 3.5 mm, while the other components retain Q235 steel and unchanged structural dimensions. Using finite element simulation, collisions involving small passenger cars, medium trucks, and buses are simulated, and performance comparisons are conducted based on vehicle trajectory and guardrail deformation. For the small passenger car scenario, risk quantification indicators—Acceleration Severity Index (ASI), Theoretical Head Impact Velocity (THIV), and Post-impact Head Deceleration (PHD)—are introduced to assess occupant injury. The results demonstrate that Scheme 2 not only meets the required protection level but also significantly reduces occupant risk for small passenger cars, lowering the injury rating from Class C to Class B. Moreover, the overall structural mass is reduced by approximately 1407 kg per kilometer, with material costs decreased by about RMB 10,129, demonstrating favorable economic efficiency. The proposed structural optimization not only effectively mitigates small car underride and improves multi-vehicle protection performance but also provides the industry with a novel guardrail geometric design directly applicable to engineering practice. The technical approach of enhancing material strength and reducing component thickness also offers a feasible reference for lightweight design, material savings, and cost optimization of guardrail systems, contributing significantly to improving the safety and sustainability of road transportation infrastructure. Full article
(This article belongs to the Special Issue Traffic Safety Measures and Assessment: 2nd Edition)
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19 pages, 3105 KB  
Article
A Longitudinal Survey Exploring the Psychological Determinants of Concealed Smartphone Use While Driving: Insights from an Expanding Theory of Planned Behavior
by Qi Zhong, Rong Han, Jiaye Chen and Chunfa Sha
Appl. Sci. 2025, 15(19), 10582; https://doi.org/10.3390/app151910582 - 30 Sep 2025
Viewed by 943
Abstract
Concealed smartphone use while driving (CSUWD), a prevalent and covert form of distracted driving, poses significant threats to road safety. However, the psychological determinants underlying this illegal behavior remain underexplored. A two-wave longitudinal study based on the expanding theory of planned behavior (TPB) [...] Read more.
Concealed smartphone use while driving (CSUWD), a prevalent and covert form of distracted driving, poses significant threats to road safety. However, the psychological determinants underlying this illegal behavior remain underexplored. A two-wave longitudinal study based on the expanding theory of planned behavior (TPB) investigates the intention and prospective behavior of CSUWD in China. In the first wave, 256 respondents assessed the standard TPB constructs, alongside extended constructs of descriptive norms, moral norms, and perceived risks. Subsequently, 156 participants reported their actual behavior in the second wave. Hierarchical multiple regression results revealed that the traditional TPB variables accounted for 57.1% of intention variance and 45.2% of behavior variance, while extended variables contributed an additional 11.7% to intention variance. All variables, except perceived crash risk, emerged as significant determinants of intention. Notably, the perceived risk of being caught and fined inversely correlated with intention, suggesting a potential disinhibition effect. Both perceived behavioral control and intention were significant determinants of subsequent behavior. The findings underscore the validity of TPB in predicting CSUWD, informing the design of non-legal interventions (e.g., public education advertisement, road awareness campaigns, and technological interventions) to mitigate CSUWD-related distracted driving and promote sustainable transportation systems. Full article
(This article belongs to the Special Issue Traffic Safety Measures and Assessment: 2nd Edition)
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Review

Jump to: Research

35 pages, 5864 KB  
Review
The State of Practice in Application of Natural Language Processing in Transportation Safety Analysis
by Mohammadjavad Bazdar, Hyun Kim, Branislav Dimitrijevic and Joyoung Lee
Appl. Sci. 2026, 16(9), 4223; https://doi.org/10.3390/app16094223 - 25 Apr 2026
Viewed by 625
Abstract
This paper provides a systematic review of recent applications of NLP methods for analyzing traffic crash reports, with a focus on estimating crash severity, crash duration, and crash causation. The review covers prior research using probabilistic topic modeling methods such as LDA, STM, [...] Read more.
This paper provides a systematic review of recent applications of NLP methods for analyzing traffic crash reports, with a focus on estimating crash severity, crash duration, and crash causation. The review covers prior research using probabilistic topic modeling methods such as LDA, STM, and hierarchical Dirichlet processes in addition to research using transformer-based language models, which include encoder-based models like BERT and PubMedBERT as well as decoder-based models like GPT, GPT2, ChatGPT, GPT-3, and LLaMA. The review starts with a systematic literature selection process with predefined inclusion criteria. We categorize the reviewed studies into the following application areas: crash severity prediction, risk factor identification in crashes, and road safety analysis. The results show several complementary advantages of using different NLP techniques to achieve different analytical goals. Topic models allow for interpretable and exploratory pattern discovery, while encoder models are well-suited for structured prediction problems. Decoder models have the additional flexibility to perform zero-shot and few-shot reasoning, which makes them useful for reasoning about under-sampled or under-reported data. Across the literature, hybrid methods that combine text and structured data outperform individual methods in terms of prediction accuracy and broad applicability. Challenges across the literature include class imbalance, lack of standardization in preprocessing and evaluation methods, and the tradeoff between prediction accuracy and interpretability of prediction models. These findings highlight the importance of aligning model selection with data availability and operational constraints, pointing toward future research directions in hybrid modeling frameworks, standardized evaluation protocols, and real-world deployment of NLP-driven traffic safety systems. Full article
(This article belongs to the Special Issue Traffic Safety Measures and Assessment: 2nd Edition)
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