A real-time dynamic pricing simulation for urban parking spaces using **Python**, **Pandas**, **Numpy**, **Pathway**, and **Bokeh**. It adjusts parking prices based on demand, occupancy, traffic conditions, queue lengths, and real-time events, with smooth and explainable pricing changes.
| Category | Technology |
|---|---|
| Language | Python 3.12 |
| Data Processing | Pandas, Numpy |
| Streaming Engine | Pathway |
| Visualization | Bokeh |
| Environment | Google Colab |
| Version Control | Git, GitHub |
- Data Ingestion
- Streaming CSVs with columns: occupancy, capacity, queue length, traffic, special day indicators
- Powered by Pathway
- Preprocessing
- Clean string values
- Map traffic conditions to numeric scores
- Normalize demand features
- Pricing Models
- Baseline: Linear price based on occupancy
- Demand-Based: Adjusted by queue, traffic, and special day indicators
- Competitive: Incorporates competitor pricing and proximity
- Dynamic Simulation
- Prices updated in real-time
- Bounded by minimum and maximum limits
- Visualization
- Real-time Bokeh charts for each parking space
Parking CSV -> Pathway Engine -> Preprocessing + Pricing Logic -> Bokeh Visuals