Road safety is a critical issue that affects communities worldwide. This project presents a Road Accident Analytics Dashboard, built in Tableau, to analyze and visualize road accident data. The dashboard provides valuable insights into accident trends, severity levels, vehicle involvement, and geographical patterns, empowering stakeholders to make data-driven decisions for improving road safety.
Live Dashboard 🔗: Tableau Public - Road Accident Analysis
✅ Tableau – Data Visualization & Dashboard Creation
✅ Excel – Data Cleaning & Transformation
The dashboard aims to provide insights into the following aspects:
✔️ Total Casualties & Total Accidents – Current Year vs. YoY Growth
✔️ Casualties by Accident Severity – Fatal, Serious, and Slight Injuries
📊 Casualties by Vehicle Type – Understanding vehicle-wise accident impact
📅 Monthly Trends – Comparison of casualties between Previous Year & Current Year
🛣️ Casualties by Road Type – Analyzing accident-prone road categories
🌍 Geospatial Analysis – Casualties by Area/Location (Urban vs. Rural)
🌙 Day vs. Night Accidents – Evaluating the role of lighting conditions
The dataset was sourced from publicly available accident reports and was prepared through data cleaning & transformation using Excel. The key steps involved:
Dataset- Accident Data
Kaggle data- Accident Data
✔️ Handling NULL values, blanks, and errors
✔️ Removing duplicates & irrelevant columns
✔️ Ensuring data consistency (correct formats & types)
✔️ Creating new attributes (e.g., Year, Month) for better analysis
The analysis was structured to explore accident patterns and relationships through interactive dashboards in Tableau.
📌 Pivot Tables & Charts were used for:
✅ Analyzing accident severity & trends
✅ Identifying high-risk areas & road types
✅ Visualizing time-series patterns of accidents
🔗 Explore the Interactive Dashboard: Tableau Public - Road Accident Analysis
📈 Total Casualties: A staggering 417,883 casualties were recorded in two years.
📅 Peak Accident Months: October & November saw the highest casualties, while January & February had the lowest.
🚗 Vehicle Type Analysis: Car accidents accounted for 79.8% of total casualties.
🩸 Accident Severity: 84.1% of casualties were categorized as slight, while 1.7% were fatal.
🛣️ Road Type Analysis: Single Carriageway roads had the highest accident rates.
🌧️ Road Surface Conditions: 67% of accidents occurred on dry roads.
🏙️ Urban vs. Rural Accidents: 61% of casualties occurred in urban areas.
☀️ Lighting Conditions: 73% of casualties happened in daylight.
🔹 Peak Accident Months (Oct & Nov): Strengthen traffic monitoring and road safety campaigns during high-risk months.
🔹 Car Accidents: Implement awareness programs and enforce strict driving regulations for car drivers.
🔹 High-Risk Roads: Upgrade Single Carriageways to double lanes wherever possible.
🔹 Road Surface Improvement: Invest in road maintenance to enhance safety conditions.
🔹 Urban Safety Measures: Implement better pedestrian controls and traffic management in urban areas.
🔗 GitHub: mayur-42
🔗 Tableau Public: Mayur Jambe
🔗 LinkedIn: Mayur Jambe
🚀 This project highlights the power of data-driven insights in improving road safety!
⭐ If you found this project helpful, don’t forget to give it a star on GitHub! ⭐