🚗 Data Analysis on Traffic Accidents

📌 Dataset Information

• 📂 Source: Kaggle
• 🔗 Link: Traffic Accidents Dataset
• 🎯 Purpose: Analyze traffic accident trends and derive insights to inform public safety strategies and policy decisions.

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❗ Problem Statement

Traffic accidents are a leading cause of death and injury worldwide, resulting in millions of lives lost or impacted each year.

📉 According to the World Health Organization (2023), approximately 1.19 million people die each year due to road traffic crashes, and tens of millions more suffer non-fatal injuries, often with long-term consequences.
(Source: WHO Global Status Report on Road Safety, 2023)

In the U.S. alone, speeding, distracted driving, and adverse weather continue to be major contributors to fatal crashes. By analyzing patterns in accident data, we can uncover critical risk factors, high-risk timeframes, and environmental triggers — helping policymakers, urban planners, and law enforcement develop more effective prevention strategies.

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📖 Table of Contents

• 🏆 Event Participation
• 📊 Analysis Objectives
• 🛠️ Methodology
• 🔑 Key Findings
• 🚦 Preventive Measures
• 🏁 Conclusion
• 📜 License

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🏆 Event Participation

• 🏛️ Competition: Spring 2025 Datathon hosted by Bits
• 🥉 Achievement: Honorable Mention (3rd Place)
• 👥 Team Members: Preet Patel, Azra Bano, Avani Kadlaskar, and Prisha Barot

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📊 Analysis Objectives

• 🔍 Identify Common Causes: Environmental, human, and vehicular factors contributing to accidents
• ⏳ Determine High-Risk Timeframes: Analyze time-of-day, day-of-week, and seasonal trends
• 🏙️ Compare Urban vs. Rural Patterns
• 🌧️ Study Weather Impact on Severity

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🛠️ Methodology

🖥️ Tools Used

• R Programming Language
  • For data cleaning, wrangling, visualization, and statistical analysis

📊 Exploratory Data Analysis (EDA)

• 📌 Accident frequency by hour/day/month
• 🌦️ Correlation between weather (rain, fog, snow) and accident severity
• 🚥 Location-based comparisons (urban vs. rural)
• 👤 Behavioral indicators like speeding or distracted driving

🔬 Example R Code for Data Import

# Load dataset
traffic_data <- read.csv("accidents.csv")

# Quick summary
summary(traffic_data)

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🔑 Key Findings

• ⏱️ Peak Accident Hours: Higher occurrences during rush hours (7–9AM, 4–7PM) and late nights (11PM–2AM)
• 🌧️ Weather Impact: Rain and fog correlate with a significant increase in accident rates
• 🏙️ Urban vs. Rural: Urban areas have more frequent accidents, while rural crashes tend to be more severe
• 🚗 Human Factors: Distracted driving and speeding remain top behavioral risks

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🚦 Preventive Measures

📜 Policy Recommendations:

• 🛂 Enhanced traffic monitoring using sensors and cameras in high-risk zones
• 🚦 Stricter speed regulation enforcement during peak accident times
• 📱 Awareness campaigns targeting distracted driving and impaired driving

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🏁 Conclusion

The data reveals that traffic safety is deeply influenced by time, weather, and human behavior. With these insights, stakeholders — from local governments to national agencies — can adopt data-driven strategies to reduce accidents, save lives, and create safer roads for everyone.

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📜 License

This project is open-source and available for educational and research use.