🚗 Dynamic Parking Price Prediction Pipeline

This project implements a dynamic pricing model for parking spots using real-time and historical parking data.
It adjusts parking prices automatically based on multiple factors like occupancy, queue length, nearby traffic, vehicle type, special days, and competitor prices — ensuring optimal utilization and revenue.

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📌 Overview

Parking spaces in cities face dynamic demand throughout the day. Static pricing leads to inefficiencies — underutilized spots or overcrowded cheap spots.
This pipeline reads parking data, calculates baseline prices, adjusts them based on demand drivers and nearby competitors, and outputs optimal prices in real-time.

Key features:

• Parses real parking data with robust error handling.
• Uses Haversine distance to find nearby competitors.
• Dynamically adjusts prices with a competitive strategy.
• Modular, fully logged, and production-ready.

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⚙️ Tech Stack

Tech	Purpose
Python 3.x	Core scripting
Pathway	Data pipelines, UDFs, joins
pandas	(Optional for local CSV checks)
Logging	Debugging and audit trail
Mermaid	Architecture diagram
CSV	Input & output format

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🗺️ Architecture Diagram

flowchart TD
    A[📂 dataset.csv] --> B[📥 Read CSV with Schema]
    B --> C[🕓 Parse DateTime]
    C --> D[⚙️ Baseline Price Calculation]
    D --> E[📈 Demand Calculation]
    E --> F[💰 Demand-based Price]
    F --> G[🔀 Competitor Cross-Join]
    G --> H[📏 Haversine Distance]
    H --> I[🚦 Filter Nearby Competitors]
    I --> J[📊 Group & Aggregate Competitor Prices]
    J --> K[⚖️ Final Price Adjustment]
    K --> L[📤 Output: CSV Export]
    L --> M[✅ Pipeline Run]

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