venkat-a / unveiling-patterns-in-traffic-crashes-a-data-driven-approach-to-safer-roads

By integrating geographical data analysis and statistical modeling, CTCA aims to inform strategies for reducing crash rates and enhancing road safety. This initiative combines innovative data processing techniques with advanced analytics to offer actionable recommendations for policymakers, urban planners, and public safety organizations.

Unveiling-Patterns-in-Traffic-Crashes-A-Data-Driven-Approach-to-Safer-Roads

By integrating geographical data analysis and statistical modeling, CTCA aims to inform strategies for reducing crash rates and enhancing road safety. This initiative combines innovative data processing techniques with advanced analytics to offer actionable recommendations for policymakers, urban planners, and public safety organizations. Description

The Comprehensive Traffic Crash Analysis (CTCA) project is an in-depth investigation into traffic crashes, emphasizing the exploration of crash severity, fatalities, and the significant role of road conditions. By analyzing varied datasets, including detailed geographical information and road attributes, this project seeks to uncover underlying patterns and predict crash occurrences. The insights gained aim to support the enhancement of road safety measures and inform public policy.

Installation

Prerequisites: Ensure you have Python (version 3.8 or newer) installed on your system. Additional software like Tableau may be required for visualizations. Dependencies: Install all necessary Python libraries using the command: bash Copy code pip install -r requirements.txt A requirements.txt file will be provided, containing all the libraries needed for running the analyses, such as Pandas, NumPy, Scikit-learn, and Matplotlib. Data Files: Download the provided datasets and place them in the designated data directory within the project folder. Usage

Data Loading:

Begin by loading the datasets using the provided scripts or Jupyter notebooks. Analysis and Prediction: Follow the instructions in the Data Engineering_Information_Pipelines_Part2_Prediction.ipynb notebook for steps on running the predictive models. Visualization: Utilize the Tableau workbook files (*.twb) for generating visual insights into the crash data. Contributing

Contributions to the CTCA project are welcome! Please refer to our contribution guidelines for information on how to submit pull requests, propose bug fixes, or suggest new features. Ensure your contributions adhere to our coding standards and are accompanied by appropriate documentation.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Credits

The CTCA project is made possible thanks to the efforts of our dedicated team, contributors from the open-source community, and the utilization of public datasets provided by local government agencies and road safety organizations.