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This repository includes a dashboard created in Excel and PowerBI and has the analysis of Road Accident cases in 2021 and 2022 in England

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🚦 Road Accident Analysis Dashboard

📌 Overview

This project presents an interactive Power BI dashboard for analyzing road accidents and casualties in the UK (2022).
The dashboard provides insights into accident severity, vehicle types involved, casualty demographics, road conditions, and spatio-temporal patterns of accidents.

The goal is to help identify critical risk factors and propose data-driven recommendations for road safety improvements.

📊 Key Metrics (2022)

  • Total Casualties: 195.7K (▼ -11.89%)
  • Total Accidents: 144.4K (▼ -11.70%)
  • Fatal Casualties: 2,855 (▼ -33.29%)
  • Serious Casualties: 27.0K (▼ -18.19%)
  • Slight Casualties: 165.8K (▼ -10.65%)

📷 Dashboard Preview

dashboard

📈 Dashboard Insights

🚘 Casualties by Vehicle Type

  • Cars → 155,804 (largest contributor).
  • Motorbikes → 15,610 casualties.
  • Trucks → 19,505 casualties.
  • Buses → 6,573 casualties.
  • Others (including vans, agro vehicles) form a smaller portion.

👉 Cars dominate accident involvement, requiring stronger urban driving safety measures.

🛣️ Road Type & Junction Control

  • Single Carriageways → Highest casualties & accidents.
  • Dual Carriageways & Roundabouts → Moderate contribution.
  • Slip roads → Least contribution.
  • Casualties highest where no junction control is present.

👉 Improved traffic management & signage could reduce risks.

🌙 Light Conditions

  • Daytime Accidents: 73.45%
  • Nighttime Accidents: 26.55%

👉 Most accidents occur during the day due to higher traffic volume.

📅 Day of Week Trends

  • Friday & Thursday show the highest accident volumes.
  • Weekends (Saturday, Sunday) show slightly lower but still significant accidents.

👉 End-of-week traffic patterns (commute + leisure travel) increase accident likelihood.

📍 Location & Area Type

  • Urban Areas: 61.91% of accidents.
  • Rural Areas: 38.09% of accidents.

👉 Urban centers remain high-risk zones due to dense traffic, but rural areas still account for a notable share.

🛠️ Tools & Technologies

  • Power BI → Interactive data visualization and reporting
  • Excel / CSV → Data source integration
  • Geospatial Analysis → Map visuals for location-based insights

📌 Key Insights Summary

  • Casualties and accidents declined in 2022, but car-related accidents remain dominant.
  • Single carriageways are the most accident-prone road type.
  • Fridays and Thursdays are the riskiest days for road travel.
  • Urban areas account for the majority of accidents, though rural accidents tend to be more severe.

🚀 Recommendations

  1. Targeted Road Safety Campaigns → Focus on urban car drivers and single carriageway safety.
  2. Junction Control Improvements → More signals, signage, and roundabout designs.
  3. Time-Specific Interventions → Extra safety measures on Fridays & Thursdays.
  4. Night-time Safety → Enhanced street lighting and stricter DUI enforcement.
  5. Rural Accident Mitigation → Improve emergency response & rural road infrastructure.

👨‍💻 Project By:

Dipean Dasgupta
Data Analyst and AIML Enthusiast

About

This repository includes a dashboard created in Excel and PowerBI and has the analysis of Road Accident cases in 2021 and 2022 in England

Topics

Resources

Stars

1 star

Watchers

1 watching

Forks

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