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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Road traffic accident severity prediction using machine learning |
| Authors Name: | SIVABRAHMAM TALLOJU , SUNEEL KUMAR DUVVURI |
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IJRTI_184233
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| Published Paper Id: | IJRTI2210015 |
| Published In: | Volume 7 Issue 10, October-2022 |
| DOI: | |
| Abstract: | Abstract This study performed a scientometric analysis of studies on traffic accidents in India between 1977 and 2020. It sought to look at the different types of publications and how they were cited and used, the growth in publications and citations year over year, the most popular journals, the keywords that authors preferred to use, the collaboration of Indian authors, the authorship pattern and the most prolific authors, and the top contributing organisations. 1,132 research articles were published throughout 44 years of study and included in the Web of Science (WoS) bibliographic collection. A study found that scholarly literature has made considerable progress, with the number of publications rising from one (0.08%) in 1977 to 182 (16.07%) in 2018.The project’s objective is to examine traffic accidents in India at the national, state, and metropolitan city levels. According to the data, the distribution of Traffic-related fatalities and injuries in India differs by age, gender, month, and Time. The population group most at risk is 30 to 59 years old, while males are more likely than females to die or sustain injuries. Additionally, severe weather and working hours are when car accidents are comparatively more common. The probability of fatalities varies greatly between states and localities, according to analysis of road accident scenarios at the state and municipal level. The risk of death exceeds the national average in 16 of the 35 states and union territories. Although the number of road accidents in India’s metropolises is slightly lower, nearly 50% of the cities have a higher risk of fatalities than their metropolitan counterparts. Without greater efforts and fresh ideas, India’s overall number of Traffic fatalities is predicted to surpass 250,000 by 2025.The majority of academic works (740, 65.37%) were published as articles. The number of publications climbed quickly from 11 (0.97%) in 2006 to 182 (16.07%) in 2018, The most active year for the researchers. 392.95 citations were made to an average of 25.73 published documents annually. Most of the publications—108, or 30%—were published in journal of Evaluation of Medical and Dental Sciences. The keyword “Trauma” was used the most frequently. The majority of articles (83.38%) on road traffic accidents (RTA) were authored by Indian authors, either alone or in conjunction with local authors. The Indian Institute of Technology made a significant contribution by publishing 120 documents (10.60%). |
| Keywords: | adaboost,confusion matrix,gradiant boost,svm,logistic regression. |
| Cite Article: | "Road traffic accident severity prediction using machine learning", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 10, page no.90 - 96, October-2022, Available :http://www.ijrti.org/papers/IJRTI2210015.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 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: IJRTI2210015
Registration ID:184233
Published In: Volume 7 Issue 10, October-2022
DOI (Digital Object Identifier):
Page No: 90 - 96 Country: Rajahmundry,East godavari, Andhrapradesh, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2210015 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2210015 |
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