Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{175966,
        author = {Geetanjali Kande and Trupti Bhase and Divya Karande and Apeksha Badhe},
        title = {Identification of Traffic accident pattern using Cluster Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {4726-4734},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175966},
        abstract = {Using cluster analysis, this research seeks to find and examine traffic accident trends. The location, time, weather, severity, and type of road are some of the variables that this machine learning technique uses to categorise comparable accidents. Finding underlying patterns and relationships in traffic accident data is intended to help guide measures aimed at enhancing road safety and lowering the number of fatalities and injuries caused by traffic. Using a sizable dataset of traffic accident records and unsupervised learning techniques, this study finds unique clusters that highlight similarities in the causes and conditions of accidents. The findings provide important information on accident hotspots, time patterns, and risk variables that can direct the creation of focused interventions like better road design, traffic control, and safety regulations. It is anticipated that these findings will Providing evidence-based decision-making for policymakers, urban planners, and transportation authorities looking to reduce the hazards of traffic accidents.},
        keywords = {Road safety, cluster analysis, unsupervised learning, traffic accidents, and accident patterns.},
        month = {April},
        }