https://github.com/johannaschmidle/road-collisions-project

Understanding Accident Severity for Effective Road Management [Excel]
https://github.com/johannaschmidle/road-collisions-project

data-analysis data-visualization excel pivot-tables traffic-analysis

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Understanding Accident Severity for Effective Road Management [Excel]

• Host: GitHub
• URL: https://github.com/johannaschmidle/road-collisions-project
• Owner: johannaschmidle
• Created: 2024-07-10T00:34:30.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-07-19T18:38:09.000Z (4 months ago)
• Last Synced: 2024-07-19T23:16:14.108Z (4 months ago)
• Topics: data-analysis, data-visualization, excel, pivot-tables, traffic-analysis
• Homepage:
• Size: 48.1 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Road Collisions Analysis (2019 - 2022)

Road accident dashboard for years 2019 - 2022 in the UK.

   



    



## Motivation

Understanding Accident Severity for Effective Road Management.   

**Goal:**  Gain insights into the patterns and trends of road accidents in the UK from 2019 to 2022 to inform policy-making and improve road safety.   

## Task List

1. Clean Database (_Cleaned table:_ [AccidentTable.csv.zip](https://github.com/johannaschmidle/Road-Collisions-Project/blob/main/AccidentTable.csv.zip))

2. Create Dashboard ([AccidentDash.xlsx](https://github.com/johannaschmidle/Road-Collisions-Project/blob/main/AccidentDash.xlsx))

## Metrics and Dimensions

- **Total** number of accidents

- **Accident Severity:** Accidents and percentage of total with respect to accident severity

- **Vehicle:** Total accidents by type of vehicle

- **Accidents per Year:** Monthly trend showing a comparison of accidents for the current year and the previous year

- **Road Type:** accidents by road type

- **Light Condition:** accidents by day/night

- **Road Condition:** Distribution of total casualties by road condition

- **Location/Area:** The type of area where the collision takes place

- Relationship between accidents by area/location and by day/night

  

## Summary of Insights

  

#### Accident Severity

- The majority of accidents are _slight_, accounting for 85.3% of the total.

- Fatal accidents constitute only 1.3% of the total.

- Serious accidents make up 13.4% of the total.

#### Vehicle

- Regular cars are most frequently involved in accidents, followed by motorcycles.

#### Accidents per year 

- Accident rates are consistently high from May to July across all four years.

- Most years see a peak in accidents in November.

- February typically has the lowest number of accidents each year.

#### Road Type

- Single-carriageway roads witness the highest number of collisions (492.1K).

- The least amount of collisions occur on a slip road

#### Light Condition

- The majority of collisions occur during daytime (approximately 75%)

#### Road Condition

- Most collisions occur on dry roads (447.8K)

- Collisions on flooded roads are rare (1.0K), likely due to the infrequency of floods.

#### Location/Area

- The majority of collisions occur in rural areas, likely due to the higher population density in these areas.

## Recommended Next Steps

- Collaborate with Stakeholders (Department for Transport, Road Safety Corps, Traffic Management agencies, etc.)

- Engage with local authorities, transport agencies, and road safety organizations to share insights and collaborate on safety initiatives.

- Use the findings to support grant applications and funding requests for road safety projects.

## Data

The dataset used in this project is available publicly on Kaggle: [https://www.kaggle.com/datasets/nezukokamaado/road-accident-casualties-dataset](https://www.kaggle.com/datasets/nezukokamaado/road-accident-casualties-dataset)

## Technologies

- Excel