This project focuses on analyzing public commute data, specifically cab rides, to provide comprehensive insights into traffic conditions and ride patterns over time. The primary objectives of the project are:
Traffic Management: Providing actionable insights for local traffic agencies to enhance traffic flow and reduce congestion. Fleet Optimization: Assisting cab companies in optimizing their fleet management strategies for better resource allocation and operational efficiency. Urban Planning: Helping city planners understand commuting trends to make informed decisions about infrastructure development. Environmental Impact: Assessing the environmental impact of commuting patterns to promote sustainable transportation solutions. Customer Behavior: Understanding customer preferences and behavior to improve service offerings and user experience. The dataset used in this project is sourced from Kaggle and contains detailed information about Uber and Lyft rides in Boston, MA. The dataset can be accessed here.
Features Traffic Analysis: Evaluate traffic conditions in various regions over different periods, identifying bottlenecks and high-traffic zones. Ride Pattern Analysis: Understand common ride patterns, including peak times, high-demand areas, and typical ride durations. Predictive Insights: Generate predictive analytics to forecast future traffic conditions and ride demand, aiding in proactive decision-making. Heatmaps and Visualizations: Create heatmaps and other visualizations to represent traffic density, ride frequencies, and other key metrics. Comparative Analysis: Compare traffic and ride patterns between different times of the day, days of the week, and seasons. Anomaly Detection: Identify anomalies and outliers in traffic and ride data, such as unexpected surges or drops in demand. Impact Assessment: Analyze the impact of external factors (e.g., weather, events) on traffic conditions and ride patterns. Operational Recommendations: Provide recommendations for optimizing traffic signals, route planning, and fleet deployment. Prerequisites Python 3.7+ Jupyter Notebook or any other preferred IDE Required Libraries pandas numpy matplotlib seaborn scikit-learn plotly Contributing Contributions are welcome! Please follow these steps:
Fork the repository. Create a new branch (git checkout -b feature-branch). Make your changes. Commit your changes (git commit -am 'Add new feature'). Push to the branch (git push origin feature-branch). Create a new Pull Request.