International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 12 | Dec 2025
p-ISSN: 2395-0072
www.irjet.net
"CrashLens: Machine Learning Insights into Statewise Traffic Safety" Prof. Suma1, Komal2 1
Professor, Master of Computer Application, VTU, Kalaburagi , Karnataka, India Student , Master of Computer Application ,VTU, Kalaburagi , Karnataka, India ------------------------------------------------------------------------***-----------------------------------------------------------------------Families of accident victims also face long-term hardships ABSTRACT- Road traffic accidents represent one of the 2
most pressing challenges in public health, economics, and transportation management worldwide. Every year, approximately 1.3 million people die globally due to road crashes, and tens of millions more are injured or disabled. Developing nations such as India bear a disproportionate share of this burden, with the Ministry of Road Transport and Highways (MoRTH) reporting nearly 1.55 lakh deaths and more than 4 lakh accidents annually. Despite policy measures, stricter enforcement of traffic rules, and awareness campaigns, accident figures remain consistently high, creating an urgent need for data-driven analysis. Traditional approaches to accident analysis rely heavily on descriptive statistics or regression based models, which summarize trends but often fail to uncover hidden structures in the data. Machine learning, particularly unsupervised techniques, offers new opportunities to discover clusters and patterns that may not be visible through conventional methods.
due to loss of income, medical expenses, and emotional trauma. Thus, the problem of road safety in India is not merely a transportation issue but a national economic and social concern.
Keywords: The results indicate that clustering can
Traditional approaches to road safety management in India have largely relied on descriptive statistical analysis of accident data published annually by MoRTH. These reports provide valuable insights into the number of accidents, fatalities, and injuries but remain limited in scope. They do not adequately capture underlying relationships or reveal hidden patterns in the data.
Several contributing factors are repeatedly cited in accident reports. Speeding, non-compliance with helmet and seatbelt regulations, distracted driving, and driving under the influence of alcohol remain leading behavioral causes. The lack of strict enforcement of traffic laws worsens these issues, especially in rural and semi-urban areas where road monitoring is minimal. Additionally, inadequate emergency response systems and delays in providing timely medical care significantly increase the fatality rate. Poor road design, insufficient signage, and lack of pedestrian-friendly infrastructure also add to the growing list of causes. This multi-dimensional nature of road accidents indicates that no single solution can fully address the crisis.
successfully separate high-fatality states such as Maharashtra, Tamil Nadu, and Uttar Pradesh from lower-risk states, while DBSCAN effectively detects anomalies.
1. INTRODUCTION
2. PROBLEM STATEMENT
India’s road safety crisis is one of the most pressing challenges in its transportation sector. The issue is not only limited to the quality of road infrastructure but also strongly influenced by human behavior, weak enforcement of traffic regulations, and socio-economic disparities. The Ministry of Road Transport and Highways (MoRTH) consistently reports alarming statistics that highlight the seriousness of the problem. In 2021 alone, India recorded more than 4.1 lakh accidents, which tragically claimed around 1.55 lakh lives and injured over 3.7 lakh people. On average, this translates to 47 accidents and 18 deaths every single hour, making India one of the worst-affected countries globally in terms of road safety.
Road safety in India has become a national concern, with an average of 47 accidents and 18 deaths reported every hour. Although statistical reports highlight the scale of the problem, they do not provide sufficient insights into patterns and relationships between accident factors. Policymakers and stakeholders face significant challenges in identifying accident-prone states, prioritizing interventions, and allocating resources effectively. The absence of automated, data-driven classification systems leads to inefficient strategies, continued loss of lives, and growing economic burden. Therefore, the problem addressed by this project is the lack of advanced analytical tools to cluster Indian states into risk categories and visualize accident hotspots for informed decision-making.
The consequences of such a large number of accidents extend beyond the immediate loss of life and injuries. According to estimates from the World Bank, road traffic accidents cost India nearly 3% of its Gross Domestic Product (GDP) every year. For a developing economy, this is an enormous economic burden that diverts resources away from infrastructure growth, education, and healthcare.
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3. OBJECTIVES Data Preparation: Collect and preprocess multi-year statewise accident data (2018– 2022) to ensure quality and uniformity. Clustering Implementation: Apply K-Means and
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