https://github.com/tashi-2004/data-visualization-tableau-traffic-collision-insights

Analysis of traffic collision data using Tableau, featuring interactive visualizations that highlight trends in injuries and fatalities, contributing factors, and geographic distributions. It includes various sheets and dashboards, with recommendations for enhancing road safety. The dataset is available for further exploration.
https://github.com/tashi-2004/data-visualization-tableau-traffic-collision-insights

data-analysis data-visualization eda geospatial-analysis machine-learning predictive-modeling statistics tableau traffic-analysis

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Analysis of traffic collision data using Tableau, featuring interactive visualizations that highlight trends in injuries and fatalities, contributing factors, and geographic distributions. It includes various sheets and dashboards, with recommendations for enhancing road safety. The dataset is available for further exploration.

• Host: GitHub
• URL: https://github.com/tashi-2004/data-visualization-tableau-traffic-collision-insights
• Owner: tashi-2004
• Created: 2024-10-27T04:15:55.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2024-10-28T00:10:24.000Z (12 months ago)
• Last Synced: 2025-01-30T08:16:09.126Z (8 months ago)
• Topics: data-analysis, data-visualization, eda, geospatial-analysis, machine-learning, predictive-modeling, statistics, tableau, traffic-analysis
• Homepage:
• Size: 847 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Data-Visualization-Tableau-Traffic-Collision-Insights

This repository contains an in-depth data analysis project using Tableau to explore traffic collision data.

## Project Overview

This project analyzes traffic collision data by focusing on injuries, fatalities, contributing factors, and geographic distributions. It uses Tableau’s interactive visualization capabilities to create a series of sheets, dashboards, and an overarching story for easy exploration and understanding of the data.

## Files

- `Book1.twb`: Contains all sheets, dashboards, and story.

- `Report.pdf`: Summarizes the analysis, insights, and recommendations.

  

## Dataset

- `tashi.csv`: [Download](https://mega.nz/file/bQtlQKDY#bj5GsCbgSy_0HZGGGJYy4yQRrvuZ6VxD5NHHxQctGU4)

  

| Column Name                        | Description                                                 | Type       |

|------------------------------------|-------------------------------------------------------------|------------|

| **CRASH_DATE**                     | The date of the traffic collision                           | Object     |

| **CRASH_TIME**                     | The time of the traffic collision                           | Object     |

| **BOROUGH**                        | The borough where the collision occurred                    | Object     |

| **ZIP_CODE**                       | The ZIP code of the collision location                      | Object     |

| **LATITUDE**                       | The latitude of the collision location                      | Float64    |

| **LONGITUDE**                      | The longitude of the collision location                     | Float64    |

| **LOCATION**                       | The specific location of the collision                      | Object     |

| **ON_STREET_NAME**                 | The street name where the collision happened                | Object     |

| **CROSS_STREET_NAME**              | The name of the cross street                                | Object     |

| **OFF_STREET_NAME**                | The name of the off street                                  | Object     |

| **INJURED_PERSONS**                | The number of people injured in the collision               | Float64    |

| **KILLED_PERSONS**                 | The number of fatalities resulting from the collision       | Float64    |

| **INJURED_PEDESTRIANS**           | The number of injured pedestrians                            | Float64    |

| **KILLED_PEDESTRIANS**            | The number of pedestrian fatalities                          | Float64    |

| **INJURED_CYCLISTS**              | The number of injured cyclists                               | Float64    |

| **KILLED_CYCLISTS**               | The number of cyclist fatalities                             | Float64    |

| **INJURED_MOTORISTS**             | The number of injured motorists                              | Float64    |

| **KILLED_MOTORISTS**              | The number of motorist fatalities                           | Float64    |

| **CONTRIBUTING_FACTOR_VEHICLE_1** | The primary contributing factor identified for Vehicle 1    | Object     |

| **CONTRIBUTING_FACTOR_VEHICLE_2** | The contributing factor identified for Vehicle 2            | Object     |

| **CONTRIBUTING_FACTOR_VEHICLE_3** | The contributing factor identified for Vehicle 3            | Object     |

| **CONTRIBUTING_FACTOR_VEHICLE_4** | The contributing factor identified for Vehicle 4            | Object     |

| **CONTRIBUTING_FACTOR_VEHICLE_5** | The contributing factor identified for Vehicle 5            | Object     |

| **COLLISION_ID**                   | Unique identifier for the collision                         | Float64    |

| **VEHICLE_TYPE_1**                 | The type of Vehicle 1 involved in the collision             | Object     |

| **VEHICLE_TYPE_2**                 | The type of Vehicle 2 involved in the collision             | Object     |

| **VEHICLE_TYPE_3**                 | The type of Vehicle 3 involved in the collision             | Object     |

| **VEHICLE_TYPE_4**                 | The type of Vehicle 4 involved in the collision             | Object     |

| **VEHICLE_TYPE_5**                 | The type of Vehicle 5 involved in the collision             | Object     |

## Sheets Overview

### Sheet 1: Yearly Trend in Injuries and Fatalities

- **Columns**: Year (Crash Date)

- **Rows**: Count Distinct of Injured Persons, Count Distinct of Killed Persons

- **Purpose**: Shows the yearly trend in traffic-related injuries and fatalities, providing insight into overall road safety trends over time.

  

### Sheet 2: Hourly Impact on Cyclist Safety

- **Columns**: Hour (Crash Time)

- **Rows**: Count of Injured Cyclists, Count of Killed Cyclists

- **Purpose**: Analyzes the time of day in relation to cyclist injuries and fatalities, highlighting peak accident times.

  

### Sheet 3: Yearly Trend for Motorcyclist Injuries and Fatalities

- **Columns**: Year (Crash Date)

- **Rows**: Count of Injured Motorcyclists, Count of Killed Motorcyclists

- **Purpose**: Illustrates trends in motorcyclist injuries and fatalities over the years, providing insights into motorcyclist safety trends.

  

### Sheet 4: Pedestrian Safety by Year

- **Columns**: Year (Crash Time)

- **Rows**: Count of Injured Pedestrians, Count of Killed Pedestrians

- **Purpose**: Highlights yearly trends in pedestrian injuries and fatalities, emphasizing pedestrian safety and potential high-risk periods.

  

### Sheet 5: Impact of Contributing Factors on Pedestrian Safety

- **Columns**: Contributing Factor Vehicle 1

- **Rows**: Average of Injured Pedestrians, Average of Killed Pedestrians

- **Purpose**: Analyzes how various contributing factors impact pedestrian safety, providing insight into potential causes of accidents involving pedestrians.

  

### Sheet 6: Geographic Distribution of Injuries and Fatalities by Zip Code

- **Columns**: Zip Code

- **Rows**: Count Distinct of Injured Persons, Count Distinct of Killed Persons

- **Purpose**: Examines the geographic distribution of injuries and fatalities across different ZIP codes, identifying high-risk areas.

  

### Sheet 7: Collision Mapping by Location and Year

- **Columns**: Average Longitude

- **Rows**: Average Latitude

- **Purpose**: Plots collisions on a map by location and year, allowing for a visual analysis of collision patterns across different locations and years.

  

### Sheet 8: Average Impact on Pedestrians by Borough

- **Columns**: Borough

- **Rows**: Average of Injured Pedestrians, Average of Killed Pedestrians

- **Purpose**: Provides a borough-based analysis of pedestrian injuries and fatalities, highlighting areas with the highest impact on pedestrian safety.

  

### Sheet 9: Street-Level Analysis of Injuries and Fatalities

- **Columns**: On Street Name

- **Rows**: Sum of Killed Persons, Sum of Injured Persons

- **Purpose**: Analyzes street-level data for injuries and fatalities, helping to identify specific streets with high accident rates over different years.

  

### Sheet 10: Yearly Total of Injuries and Fatalities by Vehicle Type

- **Columns**: Year (Crash Date)

- **Rows**: Sum of Killed Persons, Sum of Injured Persons

- **Purpose**: Shows the yearly trend of injuries and fatalities by vehicle type, providing insight into vehicle-related accident trends.=

  

  

## Dashboards

In addition to the sheets, this Tableau project includes:

- **Dashboards**: Each dashboard consolidates insights from multiple sheets, allowing for interactive exploration of the data.

  







## Key Insights and Recommendations

Based on the analysis, the following insights and recommendations were made:

1. **Trends in Injuries and Fatalities**: Observed fluctuations in injuries and fatalities over the years.

2. **Time of Day Impact**: Certain times of day show higher accident rates.

3. **Vulnerable Road Users**: Cyclists and pedestrians are particularly vulnerable.

4. **Geographical Patterns**: High-risk ZIP code areas identified.

5. **Contributing Factors**: Patterns in contributing factors for injuries and fatalities.

**Recommendations** include awareness campaigns, infrastructure improvements, policy revisions for vehicle safety, data-driven enforcement, and collaboration with local agencies.

## Contact

- For any questions or suggestions, feel free to contact at [abbasitashfeen7@gmail.com]