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Safety
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Road Traffic Risk Assessment: Control and Prevention of Collisions
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Road Traffic Risk Assessment: Control and Prevention of Collisions

A special issue of Safety (ISSN 2313-576X).

Deadline for manuscript submissions: closed (30 September 2025) | Viewed by 18241

Editor

Prof. Dr. Martin Lavallière

E-Mail Website
Guest Editor
Department of Health Sciences, University of Quebec at Chicoutimi, Chicoutimi, QC G7H 2B1, Canada
Interests: road safety; traffic; driving; control; prevention; accident; crash; injury; risk
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Road traffic safety is considered to be the most common health determinant associated with the transport sector and considers the prevention of serious injuries or deaths on the road. Moreover, road traffic collisions are among the most serious threats to public safety and injury prevention. Driver safety is influenced by numerous factors, varying from those pertaining to the human factor to those affecting traffic regulation and infrastructure design or the vehicle itself.

A reduction in crashes, injuries and deaths on the roads can be achieved through certain approaches. Several behavioral changes can help to significantly reduce the number of road traffic-related accidents, including speed control, respecting and following the road traffic rules and signs, as well as avoiding unsafe behaviours—driving under the influence of alcohol or other substances, or using a mobile phone while driving. Additionally, it is essential to identify high-risk drivers before collisions occur.

This Special Issue is an opportunity for researchers to publish valuable results in road traffic safety, behavior, prevention and control.

You may choose our Joint Special Issue in IJERPH.

Prof. Dr. Martin Lavallière
Guest Editor

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Safety 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 1800 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

  • road safety
  • traffic
  • driving
  • control
  • prevention
  • accident
  • crash
  • injury
  • risk

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Published Papers (4 papers)

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Research

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17 pages, 1303 KB  
Article
Trends in Helmet Use Among Motorcycle Drivers and Passengers in Addis Ababa: A Six-Year Observational Study (2015–2020)
by Teferi Abegaz Shifaw, Wakgari Deressa, Yifokire Tefera, Nukhba Zia, Yuan Shang, Lamisa Ashraf and Abdulgafoor M. Bachani
Safety 2026, 12(1), 26; https://doi.org/10.3390/safety12010026 - 9 Feb 2026
Cited by 1 | Viewed by 2014
Abstract
Motorcycle use is rising in low- and middle-income countries, leading to more crashes. Many deaths from these crashes are preventable with correct helmet use. This study examined correct helmet use trends and factors influencing it. A roadside cross-sectional observational study was conducted in [...] Read more.
Motorcycle use is rising in low- and middle-income countries, leading to more crashes. Many deaths from these crashes are preventable with correct helmet use. This study examined correct helmet use trends and factors influencing it. A roadside cross-sectional observational study was conducted in 10 randomly selected locations across 10 sub-cities of Addis Ababa from 2015 to 2020 twice a year. Binary logistic regression analysis was performed to identify predictors of correct helmet use. Out of 39,246 drivers and 12,950 passengers observed, 75% of drivers and 26.2% of passengers wore helmets. However, according to the Ethiopian helmet law (which requires the strapped use of any helmet type), only 34.2% of observed drivers (n = 39,246) and 9.1% of observed passengers (n = 12,950) wore helmets correctly. Under the global best-practice standard (strapped use of approved helmets excluding cap helmets) was even lower at 29.6% among drivers and 6.6% among passengers. Correct use declined over six years until the 2019 reinitiation of helmet law enforcement. Among drivers, correct use was linked to full-face helmets (AOR = 1. 90, 95% CI: 1.77–2.04), police enforcement (AOR = 1.08, 95%CI: 1.02–1.14), rain (AOR = 1.26, 95% CI: 1.14–1.40), and riding on arterial roads (AOR = 1.89, 95% CI: 1.78–2.00). For passengers, being female (AOR = 1.55, 95% CI: 1.09–2.19), aged ≥18 (AOR = 2.16, 95% CI: 1.34–3.46), and riding with correctly helmeted drivers (AOR = 3.37, 95% CI: 2.88–3.95) increased correct use. The findings indicate a need for a combination of interventions, including awareness-raising campaigns, sustained enforcement, and preparing helmet standards. Full article
(This article belongs to the Special Issue Road Traffic Risk Assessment: Control and Prevention of Collisions)
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31 pages, 1941 KB  
Article
Boosting Traffic Crash Prediction Performance with Ensemble Techniques and Hyperparameter Tuning
by Naima Goubraim, Zouhair Elamrani Abou Elassad, Hajar Mousannif and Mohamed Ameksa
Safety 2025, 11(4), 121; https://doi.org/10.3390/safety11040121 - 9 Dec 2025
Cited by 2 | Viewed by 3114
Abstract
Road traffic crashes are a major global challenge, resulting in significant loss of life, economic burden, and societal impact. This study seeks to enhance the precision of traffic accident prediction using advanced machine learning techniques. This study employs an ensemble learning approach combining [...] Read more.
Road traffic crashes are a major global challenge, resulting in significant loss of life, economic burden, and societal impact. This study seeks to enhance the precision of traffic accident prediction using advanced machine learning techniques. This study employs an ensemble learning approach combining the Random Forest, the Bagging Classifier (Bootstrap Aggregating), the Extreme Gradient Boosting (XGBoost) and the Light Gradient Boosting Machine (LightGBM) algorithms. To address class imbalance and feature relevance, we implement feature selection using the Extra Trees Classifier and oversampling using the Synthetic Minority Over-sampling Technique (SMOTE). Rigorous hyperparameter tuning is applied to optimize model performance. Our results show that the ensemble approach, coupled with hyperparameter optimization, significantly improves prediction accuracy. This research contributes to the development of more effective road safety strategies and can help to reduce the number of road accidents. Full article
(This article belongs to the Special Issue Road Traffic Risk Assessment: Control and Prevention of Collisions)
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19 pages, 1760 KB  
Article
A Multilevel Spatial Framework for E-Scooter Collision Risk Assessment in Urban Texas
by Nassim Sohaee, Arian Azadjoo Tabari and Rod Sardari
Safety 2025, 11(3), 67; https://doi.org/10.3390/safety11030067 - 17 Jul 2025
Cited by 2 | Viewed by 2337
Abstract
As shared micromobility grows quickly in metropolitan settings, e-scooter safety issues have become more urgent. This paper uses a Bayesian hierarchical model applied to census block groups in several Texas metropolitan areas to construct a spatial risk assessment methodology for e-scooter crashes. Based [...] Read more.
As shared micromobility grows quickly in metropolitan settings, e-scooter safety issues have become more urgent. This paper uses a Bayesian hierarchical model applied to census block groups in several Texas metropolitan areas to construct a spatial risk assessment methodology for e-scooter crashes. Based on crash statistics from 2018 to 2024, we develop a severity-weighted crash risk index and combine it with variables related to land use, transportation, demographics, economics, and other factors. The model comprises a geographically structured random effect based on a Conditional Autoregressive (CAR) model, which accounts for residual spatial clustering after capture. It also includes fixed effects for covariates such as car ownership and nightlife density, as well as regional random intercepts to account for city-level heterogeneity. Markov Chain Monte Carlo is used for model fitting; evaluation reveals robust spatial calibration and predictive ability. The following key predictors are statistically significant: a higher share of working-age residents shows a positive association with crash frequency (incidence rate ratio (IRR): ≈1.55 per +10% population aged 18–64), as does a greater proportion of car-free households (IRR ≈ 1.20). In the built environment, entertainment-related employment density is strongly linked to elevated risk (IRR ≈ 1.37), and high intersection density similarly increases crash risk (IRR ≈ 1.32). In contrast, higher residential housing density has a protective effect (IRR ≈ 0.78), correlating with fewer crashes. Additionally, a sensitivity study reveals that the risk index is responsive to policy scenarios, including reducing car ownership or increasing employment density, and is sensitive to varying crash intensity weights. Results show notable collision hotspots near entertainment venues and central areas, as well as increased baseline risk in car-oriented urban environments. The results provide practical information for targeted initiatives to lower e-scooter collision risk and safety planning. Full article
(This article belongs to the Special Issue Road Traffic Risk Assessment: Control and Prevention of Collisions)
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Review

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28 pages, 1326 KB  
Review
A Systematic Literature Review of Cargo Loss Risks in Road Transportation: Impacts and Future Directions
by Praiya Panjee, Varunya Kaewchueaknang and Sataporn Amornsawadwatana
Safety 2025, 11(1), 20; https://doi.org/10.3390/safety11010020 - 26 Feb 2025
Cited by 10 | Viewed by 9257
Abstract
This systematic literature review aims to identify and discuss specific cargo loss risks in road transportation. The research also examines their impacts, challenges, and mitigation strategies. By synthesizing insights from 24 studies using a systematic snowballing methodology, this study categorizes risks into five [...] Read more.
This systematic literature review aims to identify and discuss specific cargo loss risks in road transportation. The research also examines their impacts, challenges, and mitigation strategies. By synthesizing insights from 24 studies using a systematic snowballing methodology, this study categorizes risks into five primary domains: Man, Method, Machine, Material, and Environment. Specifically, the review highlights major cargo loss risks within the context of road transportation. A fishbone diagram illustrates the multifactorial interactions that contribute to cargo loss. Emerging technological solutions, such as predictive analytics, IoT-enabled monitoring, and advanced packaging designs, are explored as key strategies to mitigate these risks. The findings emphasize the need for a comprehensive approach to enhance road transport safety, reduce cargo loss, and strengthen the resilience of global supply chains. Full article
(This article belongs to the Special Issue Road Traffic Risk Assessment: Control and Prevention of Collisions)
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