https://github.com/manuethomas/traffic-accident-analysis-us

The project provides a comprehensive analysis of traffic accidents in the US from 2016-2023 aiming to identify key factors contributing to accidents. The analysis also focussed on finding features that could be used to develop a predictive model
https://github.com/manuethomas/traffic-accident-analysis-us

exploratory-data-analysis feature-engineering feature-selection matpllotlib numpy pandas seaborn

Last synced: over 1 year ago
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The project provides a comprehensive analysis of traffic accidents in the US from 2016-2023 aiming to identify key factors contributing to accidents. The analysis also focussed on finding features that could be used to develop a predictive model

• Host: GitHub
• URL: https://github.com/manuethomas/traffic-accident-analysis-us
• Owner: manuethomas
• License: apache-2.0
• Created: 2025-01-16T07:03:02.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-01-16T15:51:01.000Z (over 1 year ago)
• Last Synced: 2025-01-25T09:12:46.198Z (over 1 year ago)
• Topics: exploratory-data-analysis, feature-engineering, feature-selection, matpllotlib, numpy, pandas, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 3.26 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          




[***Click here to download detailed Report***](https://github.com/manuethomas/traffic-demo/blob/main/Report/Analysis%20of%20Traffic%20Accidents%20in%20the%20US.pdf)

## Problem Statement

The primary goal of the project was to analyze traffic accident data in the US from 2016 to 2023 using the ”US Accident (2016-2023)” dataset and find out relevant features that can be used to build a predictive system in the future.

- Analyse the primary causes of accidents and their associated risk levels

- Identify key factors that most reliably indicate the occurance of a major accident

- Find out relevant features that could help forecast both the probability and severity of accidents before they happen.

- Share the insights with stakeholders about where accidents commonly happen, patterns in casualties, and how weather and environmental conditions influence accident occurrence.

## Approach

- Dataset Overview

- Import relevant libraries

- Load the dataset

- Data Preprocessing 

    - Removing Unnecessary Features

    - Transforming Datetime Feature

    - Handling Missing Values

    - Outlier Treatment

- Exploratory Data Analysis & Feature Engineering

    - Resampling

    - Time-Based Feature Analysis

    - Location-Based Feature Analysis

    - Weather Feature Analysis

    - Point of Interest Features Analysis

- Feature Selection

- Further Analysis

- Key Insights and Recommendations

## Exploratory Data Analysis & Feature Engineering

### Time-Based Feature Analysis

- When do most severe accidents occur during the year? 

- How does accident frequency vary between weekdays and weekends?

- What time of day sees the most accidents?

- Are night accidents different from day accidents? 

### Location-Based Feature Analysis

- Which states have the highest accident counts?s

- Which states have the most severe accidents?

- Does public transit usage in a county affect accident rates?

- Which types of roads are most dangerous?

- Are accidents concentrated in specific cities?

- Which timezone sees the most accidents?

### Weather Feature Analysis

- Which weather conditions are most dangerous for driving?

- What role do visibility, pressure, and wind speed play?

### Point of Interest (POI) Feature Analysis

- How do traffic signals and crossing impact accident severity?

- Are there any specific infrastructural impact on accident severity?

## Key Takeaways

- Summer months + weekends + nighttime = Highest risk for severe accidents

- Georgia & Florida lead in severe accidents despite CA/TX/FL having most total accidents

- Less public transit = More severe accidents in a county

- Traffic signals & crossings cut accident severity significantly

- Interstate highways are hotspots for severe accidents

- Accidents spread across cities rather than concentrated in specific areas

- Night accidents are fewer but deadlier

- Rain/snow conditions notably increase accident severity

## References

- Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, Radu Teodorescu, and Rajiv Ramnath. “Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights.”

- Triveni Sangama Saraswathi Edla. "Exploratory Data Analysis (EDA) of US Accidents and Prediction of Accident Severity in San Fransisco bay area"

- Medium: Ronghui Zhou, Ph.D.""How You Can Avoid Car Accident in 2020"