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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | SOSafe (Road Accident Prediction) |
| Authors Name: | Raj Agarwal , Nikhil mahajan , Roshni Mukherjee , Pragya Saini |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_219798
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| Published Paper Id: | IJNRD2404809 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | Numerous studies have focused on accident prevention and detection, often involving the use of sensors to identify potential hazards or the analysis of accident statistics. However, this particular study delves into a novel approach by developing a system geared towards detecting ongoing accidents. This system gathers essential data from nearby vehicles and employs machine learning techniques to identify potential accident situations. Machine learning algorithms have proven effective at distinguishing abnormal behaviors from typical ones. The main aim of this research is to examine traffic patterns and flag vehicles that exhibit deviations from the norm as potential accident scenarios. The results have demonstrated the success of clustering algorithms in accident detection. The issue of fatalities and injuries resulting from accidents is a global concern that has persisted since the advent of the automobile nearly a century ago. Shockingly, it is estimated that more than 300,000 individuals lose their lives and 10 to 15 million sustain injuries in road accidents worldwide every year. Notably, statistics reveal a high mortality rate among young adults, who constitute a significant portion of the workforce. To address this critical problem, various road safety strategies and measures are imperative. The societal and economic losses stemming from road accidents are unbearable, particularly in developing countries like ours. Consequently, it has become a pressing necessity to implement an advanced traffic management system that can reduce the incidence of road accidents. By adopting simple precautionary measures based on predictions from a sophisticated system, we may effectively mitigate traffic accidents. Furthermore, to confront the distressing reality of daily traffic related fatalities, it is vital to embrace machine learning as a practical and effective approach for making informed decisions based on past experiences and the insights derived from our analysis, which can then be shared with traffic authorities. SOSafe, the system presented in this study, is poised to make a significant impact on road safety, aiming to reduce accident severity and save lives by integrating technological advancements in accident prediction and emergency response systems. |
| Keywords: | Accident detection, Machine learning, Road safety, Vehicle data, Real-time monitoring, Abnormal behavior, Emergency response, Accident prevention, Traffic accidents, Technological advancements, Predictive modeling, Proactive safety, Data analysis, Vehicle behavior, Severity reduction, Lives saved, Road traffic safety. |
| Cite Article: | "SOSafe (Road Accident Prediction)", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.i64-i68, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404809.pdf |
| Downloads: | 00043 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2404809 Registration ID: 219798 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: i64-i68 Country: Nashik, Maharashtra, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404809 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404809 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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