Analysis of Advanced Technologies to Reduce Traffic Accidents in Foggy Conditions

International Journal of Electronics and Communication Engineering |
© 2025 by SSRG - IJECE Journal |
Volume 12 Issue 1 |
Year of Publication : 2025 |
Authors : Milagros Vara-Teodoro, Oscar Arana-Huanca, Alicia Alva-Mantari, Ana Huamani-Huaracca, Sebastián Ramos-Cosi |
How to Cite?
Milagros Vara-Teodoro, Oscar Arana-Huanca, Alicia Alva-Mantari, Ana Huamani-Huaracca, Sebastián Ramos-Cosi, "Analysis of Advanced Technologies to Reduce Traffic Accidents in Foggy Conditions," SSRG International Journal of Electronics and Communication Engineering, vol. 12, no. 1, pp. 83-91, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I1P106
Abstract:
Fog is one of the factors that affect visibility on roads the most, contributing to 15% of road accidents worldwide, especially in mountainous and rural areas. In the central highlands, 30% of fatal accidents are related to these conditions, with an increase of 20% during critical months. This study aims to analyze advanced technologies, such as adaptive lighting systems, driver assistance devices and artificial intelligence algorithms, aimed at reducing accidents in foggy scenarios. The methodology used a systematic review under the PRISMA guidelines, using the Scopus and IEEE Xplore databases. After applying inclusion and exclusion criteria, 138 articles published between 2003 and 2024 were analyzed. Tools such as VOSviewer and Google Colab facilitated bibliometric analysis and trend visualization. The results showed that China leads the research with 67 publications, followed by the United States with 26 publications and India with 13 publications. A total of 80 scientific articles and 70 conference articles were identified, with a predominance in engineering (34.7%) and computer science (25%). The technologies analyzed, especially those based on artificial intelligence, showed improvements of 30%, 40%, and 22% in detection systems. Despite these advances, its implementation faces economic and infrastructural challenges, so it is suggested that international collaborations be promoted and solutions adapted to local contexts be developed to improve road safety.
Keywords:
Fog, Security, Technology, Detection, Review, Traffic.
References:
[1] Xueyan Yin et al., “Deep Learning on Traffic Prediction: Methods, Analysis, and Future Directions,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 6, pp. 4927-4943, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Ayme Condor Buitron et al., “Speeding Control and Accidents in the Peruvian Central Road,” International Journal of Data and Network Science, vol. 7, no. 2, pp. 921-926, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Jakob Peintner et al., “Mixed Reality Environment for Complex Scenario Testing,” ACM International Conference Proceeding Series, pp. 605-608, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Md. Nasim Khan, Anik Das, and Mohamed M. Ahmed, “Non-Parametric Association Rules Mining and Parametric Ordinal Logistic Regression for an In-Depth Investigation of Driver Speed Selection Behavior in Adverse Weather using SHRP2 Naturalistic Driving Study Data,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2674, no. 11, pp. 101-119, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Ankur Lohachab, and Karambir, “ECC Based Inter-Device Authentication and Authorization Scheme Using MQTT for IoT Networks,” Journal of Information Security and Applications, vol. 46, pp. 1-12, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Kun Gao et al., “Impacts of Reduced Visibility Under Hazy Weather Condition on Collision Risk and Car-Following Behavior: Implications for Traffic Control and Management,” International Journal of Sustainable Transportation, vol. 14, no. 8, pp. 635-642, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Anik Das, Ali Ghasemzadeh, and Mohamed M. Ahmed, “Analyzing the Effect of Fog Weather Conditions on Driver Lane-Keeping Performance Using the SHRP2 Naturalistic Driving Study Data,” Journal of Safety Research, vol. 68, pp. 71-80, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[8] O. Alizadeh-Choobari et al., “Temporal and Spatial Variations of Particulate Matter and Gaseous Pollutants in the Urban Area of Tehran,” Atmospheric Environment, vol. 141, pp. 443-453, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Yina Wu et al., “Combined Connected Vehicles and Variable Speed Limit Strategies to Reduce Rear-End Crash Risk Under Fog Conditions,” Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, vol. 24, no. 5, pp. 494-513, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Wooseong Kim, and Kyungho Ryu, “Autocoin: Secure Content Sharing Based on Blockchain for Vehicular Cloud,” Electronics (Switzerland), vol. 10, no. 12, pp. 1-23, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Jianyang Song et al., “Study on Risk Prediction Model of Expressway Agglomerate Fog-Related Accidents,” Atmosphere, vol. 14, no. 6, pp. 1-16, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Shuya Sun, Jiangbi Hu, and Ronghua Wang, “Correlation between Visibility and Traffic Safety Visual Distance in Foggy Areas during the Daytime,” Traffic Injury Prevention, vol. 22, no. 7, pp. 514-518, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Felipe Calsavara, Felipe Issa Kabbach Junior, and Ana Paula C. Larocca, “Effects of Fog in a Brazilian Road Segment Analyzed by a Driving Simulator for Sustainable Transport: Drivers’ Visual Profile,” Sustainability, vol. 13, no. 16, pp. 1-13, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] W. Feng et al., “Modeling and Analysis of Traffic State of Regional Freeway Network in Fog Region,” Journal of Wuhan University of Technology, Transportation Science and Engineering, vol. 45, no. 1, pp. 93-98, 2021.
[Google Scholar]
[15] Wasi Yazdani, Mohd Shamim Ansari, and Lamaan Sami, “A Bibliometric Analysis of Mandatory Corporate Social Responsibility Using RStudio: Based on Scopus Database,” International Journal of Professional Business Review, vol. 7, no. 6, pp. 1-28, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Muhammar Khamdevi, “A Systematic Literature Review of Architecture-Related Dew and Fog Harvesting,” Visions for Sustainability, no. 20, pp. 13-45, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Dwi Fitria Al Husaeni, and Asep Bayu Dani Nandiyanto, “Bibliometric Using Vosviewer with Publish or Perish (Using Google Scholar Data): From Step-by-Step Processing for Users to the Practical Examples in the Analysis of Digital Learning Articles in Pre and Post Covid-19 Pandemic,” ASEAN Journal of Science and Engineering, vol. 2, no. 1, pp. 19-46, 2022.
[Google Scholar] [Publisher Link]
[18] Xue Ding, and Zhong Yang, “Knowledge Mapping of Platform Research: A Visual Analysis using VOSviewer and CiteSpace,” Electronic Commerce Research, vol. 22, no. 3, pp. 787-809, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Abidin Kemec, and Aysenur Tarakcıoglu Altınay, “Sustainable Energy Research Trend: A Bibliometric Analysis Using VOSviewer, RStudio Bibliometrix, and CiteSpace Software Tools,” Sustainability, vol. 15, no. 4, pp. 1-21, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] William Vallejo, Carlos Diaz-Uribe, and Catalina Fajardo, “Google Colab and Virtual Simulations: Practical e-Learning Tools to Support the Teaching of Thermodynamics and to Introduce Coding to Students,” ACS Omega, vol. 7, no. 8, pp. 7421-7429, 2022.
[CrossRef] [Google Scholar] [Publisher Link]