This project aims to analyze and predict traffic volume using historical traffic data. The dataset contains traffic counts for cars, bikes, buses, and trucks, along with corresponding timestamps and traffic situations.
- Data preprocessing and cleaning
- Feature engineering
- Exploratory Data Analysis (EDA) with visualizations
- Time series analysis
- Basic predictive modeling using Linear Regression
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
- Data loading and initial exploration
- Feature engineering (datetime manipulation, traffic situation classification)
- Visualization of traffic patterns (time series, hourly distribution, weekly patterns)
- Correlation analysis
- Creation of lagged features for time series forecasting
- Implementation of a Linear Regression model for traffic prediction
- Identified hourly and daily traffic patterns
- Analyzed the distribution of different vehicle types
- Explored correlations between various traffic-related features
- Developed a basic predictive model for total vehicle count
- Implement more advanced time series forecasting models (e.g., ARIMA, Prophet)
- Incorporate external factors (weather, events) for more accurate predictions
- Develop an interactive dashboard for real-time traffic monitoring
- Route optimization for reducing traffic