Skip to content

rakibhasanrob/Road_Accident_Analysis_EXCEL_Project

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

2 Commits
 
 

Repository files navigation

Road Accident Analysis

Introduction:

The aim of this project is to analyze road accident data to identify key trends and factors contributing to road accidents in 2021-2022. Understanding these metrics is crucial for developing targeted safety measures and reducing casualties.

Objectives:

  • Identify the types of vehicles most involved in accidents.
  • Analyze the severity of casualties by vehicle type and road conditions.
  • Determine the impact of different road and lighting conditions on accident rates.

Data Collection:

  • Data Sources: National road accident database, traffic surveillance reports.
  • Data Description: The dataset includes the number of casualties by vehicle type, road type, surface condition, lighting condition, and area type.

Methodology:

  • Data Preparation: Data cleaning involved handling missing values and normalizing accident figures across different regions.
  • Analysis Techniques: Descriptive statistics and trend analysis were performed.
  • Tools and Technologies: We used Excel for data extraction, data cleaning, analysis, and visualization.

Findings and Results:

  • Total Casualties: There were a total of 417,883 casualties, with 7,135 being fatal, 59,312 serious, and 351,436 slight.
  • Vehicle Type Insights: Cars accounted for the majority of casualties (333,485). Motorcycles and vans followed with 33,672 and 33,472 casualties, respectively.
  • Monthly Trends: Casualties fluctuated throughout the year, with a noticeable increase in certain months.
  • Road Type Insights: Single carriageway roads saw the highest number of casualties (309.7K). Dual carriageways and roundabouts also had significant numbers of casualties (67.4K and 26.8K, respectively).
  • Road Surface and Lighting Conditions: Most accidents occurred on dry roads (279.4K), with wet roads contributing to 115.3K casualties. Daylight conditions saw the highest number of casualties (305.0K), compared to darkness (112.9K).
  • Area Type Insights: Urban areas had more casualties (255.9K) compared to rural areas (162.0K).

Conclusion:

The analysis reveals that cars are the most common vehicle type involved in accidents, with the majority occurring on single carriageway roads under dry conditions during daylight. Safety measures should focus on these high-risk areas to effectively reduce the number of casualties.

References:

  • National Road Accident Database Documentation.
  • Traffic Surveillance Reports 2021-2022.

Dashboard:

Snapshot of Dashboard (RAA)

About

No description or website provided.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Contributors