Road Accident Analysis with Power BI

 

Road Accident Data Analysis Dashboard

Introduction

In this project, I developed a comprehensive Power BI dashboard to analyze road accident data spanning the years 2021 and 2022. The dataset, sourced from a CSV file, underwent rigorous cleaning using Power Query to ensure accuracy and consistency. The resulting dashboard provides critical insights into various factors contributing to road accidents, enabling stakeholders to make informed decisions and enhance road safety.

 LinkedIn - www.linkedin.com/in/abhinav-tomar-b160271a2

Key Features

1. Total Casualties

The dashboard reveals a staggering total of 418,000 casualties over the two-year period. This figure underscores the urgency of addressing road safety.

2. Casualties by Road Type

  • Single Carriageways: These roads witnessed the highest number of casualties, with 311,619 incidents. Targeted safety measures are essential for these high-risk areas.
  • Dual Carriageways: Recorded 67,368 casualties.
  • Roundabouts: Contributed to 2,828 casualties.

3. Casualties by Road Surface Condition

  • Dry Roads: Most accidents occurred on dry roads, resulting in 280,000 casualties.
  • Wet/Damp Conditions: These conditions contributed to 115,000 casualties.
  • Snow/Ice: The least number of casualties occurred under snow and ice conditions.

4. Casualties by Vehicle Type

  • Cars: Involved in the majority of accidents, with 333,000 casualties.
  • Motorcycles: Contributed to 34,000 casualties.
  • Goods Carriers: Accounted for 33,000 casualties.
  • Buses and Other Vehicles: Had comparatively lower involvement.

5. Casualties by Accident Severity and Road Type

  • Single Carriageways: Represented 75% of total casualties (312,000 incidents).
  • Dual Carriageways and Roundabouts: Had significantly fewer casualties.

6. Casualties by Light Conditions and Urban vs. Rural Areas

  • Urban Areas (Daylight): Witnessed 186,000 casualties.
  • Rural Areas (Daylight): Reported 119,000 casualties.
  • Darkness without Lighting: Had the fewest casualties.

7. Casualties by Month and Year

  • The data is segmented by month, revealing trends between 2021 and 2022.
  • January and October: Highest number of casualties.
  • April and December: Lowest casualties.

Insights and Observations

  1. High-Risk Road Types: Single carriageways demand targeted safety interventions.
  2. Weather Impact: Rain, ice, and snow significantly contribute to accidents. Caution during adverse conditions is crucial.
  3. Vehicle Types: Focused education campaigns for car and motorcycle drivers can reduce accidents.
  4. Lighting Conditions: Adequate road lighting, especially in rural areas, can enhance nighttime safety.
  5. Temporal Trends: Seasonal road safety campaigns can be strategically planned based on accident rates.

Conclusion

This Power BI dashboard serves as a vital tool for visualizing and understanding road accident data. By analyzing road type, weather conditions, vehicle involvement, and other factors, stakeholders can drive impactful actions to improve road safety. Your project exemplifies the power of data visualization in making our roads safer for everyone.

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