Development of Naïve Method to Analyse the Road Accidents Based on Data Mining Techniques

Authors

  • Atul Pandey  Sheat College of Engineering, Varanasi, Uttar Pradesh, India
  • Virendra Pratap Yadav  Sheat College of Engineering, Varanasi, Uttar Pradesh, India

Keywords:

Hierarchical Clustering, K-modes clustering, Self-Organizing Map, Three-and-a-half inches of Birch, Invite Vector Machine Support, Latent Class Clustering, Intentional Bayes

Abstract

“Road Accident is an all-inclusive calamity with the continually growing trend. In India, according to the Indian road safety campaign every minute, there is a traffic accident and about 17 people die each hour in road accidents. There are several types of car accidents such as rear-end, head-on, and rollover accidents. India, according to the Indian road safety campaign every minute, there is a traffic accident and about 17 people die each hour in road accidents. There are several types of car accidents such as rear-end, head-on, and rollover accidents. The state-recorded police reports or FIRs are the records that provide information regarding the accidents. The event may be self-reported by the individuals or documented by the state police. Using Apriori and Nave Bayesian approaches, recurrent patterns of road accidents are predicted in this research. The government or non-profit organizations might use this pattern to enhance road safety and implement preventative measures in high-accident areas. From 2020 to 2021, a total of 11,574 accidents happened on the roads in the Dehradun district. Based on the variables of accident type, road type, lightning on road, and road feature, K modes clustering found six clusters (C1–C6). Each cluster and the EDS have been used to construct rules using association rule mining. Rules with high lift values are used in the study. Using the rules for each cluster, it is possible to learn about the causes of accidents in that cluster. When compared to EDS-generated rules, this comparison reveals that EDS-generated rules do not give relevant information that may be linked to an accident. If additional features linked with an accident are accessible, more information may be discovered. We also did monthly and hourly trend analyses of all clusters and EDS to reinforce our technique. According to trends, clustering before an analysis helps us locate better and more helpful outcomes that we wouldn't otherwise be able to find.”

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2022-07-30

Issue

Volume 8, Issue 4, July-August-2022

Section

Research Articles

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How to Cite

[1]
Atul Pandey, Virendra Pratap Yadav, " Development of Naïve Method to Analyse the Road Accidents Based on Data Mining Techniques, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 4, pp.244-255, July-August-2022.