README.md

metadata

title: BTP Intelligence Engine
colorFrom: purple
colorTo: red
sdk: docker
pinned: false
app_port: 7860
license: mit

Bengaluru Traffic Police: Intelligence Engine

Flipkart Gridlock 2.0 Hackathon Submission Problem Statement 2: Event-Driven Traffic Congestion Forecasting & Dynamic Routing

The BTP Intelligence Engine is a production-grade MLOps prototype designed to simulate a live command center tool. Moving beyond standard static machine learning, it provides a comprehensive end-to-end simulation capable of predicting, visualizing, and coordinating responses to localized traffic breakdown events (both planned and unplanned) across the Bengaluru Metropolitan Area.

Key Features

1. Ultra-Premium "Cyber-Neon" Aesthetic

A beautiful, high-contrast dark-mode UI built natively on Streamlit. Features custom CSS for frosted glass metric cards, glowing crimson accents, and centralized state management for a seamless, professional experience.

2. Dual-Mode Input Controller

A robust state controller that perfectly synchronizes input commands without lag:

• Interactive Map: Click anywhere on the map to instantly set the event epicenter.
• Manual GPS: Precise latitude and longitude inputs with strict geo-fencing (Lat 12.80-13.25, Lon 77.45-77.80) to prevent coordinate drifting to the Arctic. Out-of-bounds clicks trigger non-intrusive toast notifications.

3. Live Spatio-Temporal Forecasting Pipeline

• Impact Forecaster: Utilizes advanced ML models (XGBoost) trained on temporal/spatial features to predict congestion radius and clearance times.
• Operations Optimizer: Employs NetworkX graph logic to generate dynamic routing diversions, along with deterministic models to calculate the required ground personnel and barricades.

4. Advanced Geospatial Visualizations

• Live Operational Map: Interactive Folium rendering with an overlay of the congestion zone (Red Circle), diversion points (Green Markers), and calculated polyline escape routes.
• Resource Allocation Summary Heatmap: A folium.plugins.HeatMap that mathematically distributes density coordinates (epicenter, diversion nodes, and perimeter barricades) to visually communicate the deployment footprint of BTP personnel.

5. Network Cascade Alerts & Plotly Telemetry

• Dynamic OSM Lookups: Automatically queries the OpenStreetMap API via geopy to reverse-geocode map clicks, generating hyper-local neighborhood-aware warnings.
• Perimeter Saturation Chart: A live, horizontal Plotly bar chart visually mapping the predicted stress load onto surrounding arterial roads, shifting to Crimson when capacity hits $\ge 90%$.

6. Post-Event Retrospective & MLOps Database

• Model Calibration Form: Swap the dashboard to retrospective mode. Command center operators can log the actual clearance times and variance factors directly against the AI forecast.
• SQLite Persistence: Ground-truth deltas are seamlessly committed to a local btp_intelligence.db database using native Python SQLite3.
• Live MLOps Health Card: A live diagnostic banner computes Mean Absolute Error (MAE) and Systematic Bias in real-time directly from the database to track model drift.

Tech Stack

• Frontend & App State: Streamlit, Streamlit-Folium
• Machine Learning: Scikit-Learn, XGBoost, Pandas, Numpy, Scipy
• Geospatial & Routing: Folium, NetworkX, Geopy
• Data Visualization: Plotly
• Database Persistence: SQLite3

Installation & Execution

1. Navigate to the project directory.
2. Install the dependencies:

   pip install -r requirements.txt
   

3. Train the local ML Models (Ensure binaries exist):

   python src/main.py
   

4. Launch the Intelligence Engine:

   streamlit run src/app.py
   

Design Philosophy

The system implements a "Demo Mode" initialization, instantly loading a default event profile centered around Cubbon Park (Lat 12.9716, Lon 77.5946) to prevent a cold-start blank screen during hackathon presentations.