BroLift: Smart College Carpooling Network

A high-performance system for academic commute coordination. It cross-references student verification with real-time routing to optimize campus transport and reduce structural inefficiencies in college transit.

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Executive Summary

BroLift (Autonomous Ride Intelligence) provides high-fidelity carpooling coordination. The system automates the matching of student drivers and passengers within a verified network by integrating Google Maps routing and deterministic fuel-cost inference models.

• Optimized for low-latency responses, generating route calculations within milliseconds.
• Designed as a scalable full-stack system capable of handling real-time ride tracking with optimized passenger matching.

Interface Preview

Home Dashboard	Real-time Search	Ride Management

Deployment

• Frontend & API: Deployed on Render View Live Dashboard
• Database Layer: Managed PostgreSQL instance on Render

Architecture Diagram

graph TD
    A[Client Browser] -->|HTTP/HTTPS| B(Flask Web Server)
    B -->|SQLAlchemy ORM| C[(PostgreSQL Database)]
    B -.->|Directions & Places| D[Google Maps API]
    A -.->|JavaScript Init| D
    B -->|Authentication| E(Flask-Login)

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View detailed dependency graph

graph LR
    A[Jinja2 / JS Frontend] -->|REST API Requests| B[Flask Backend]
    B -->|Schema Validation| C[(PostgreSQL / SQLite)]
    B -->|Route Generation| D[Google Directions API]
    C -->|User & Ride Data| B
    D -->|Polyline Data| B
    B -->|JSON Response| A

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System Design

• Handles verified academic ingestion pipeline (@srmist.edu.in)
• Uses spatial indexing for efficient pickup retrieval
• Optimized API responses for low latency route calculation

System Architecture & Components

• Frontend Dashboard: Responsive Material Design interface providing real-time ride discovery.
• Backend API: Python Flask layer handling ride lifecycle, orchestration, and integrations.
• Network Infrastructure: Strict SRM IST email verification combined with regex-based credential auditing.
• Routing Intelligence Mesh: High-performance inference via Google Maps API for institutional transit reasoning.
• Strategic Cost Gateway: Real-time integration with fuel price metrics for fair-split financials.

API Flow

1. Ride Creation (/rides/host): Hosts define route and capacity; data is serialized into PostgreSQL.
2. Ride Query (/rides/find): Passengers perform spatial searches against the ride database.
3. Request Logic: Passengers request joins; backend validates capacity and overlaps.
4. Financial Evaluation: Cost-per-head is computed using real-time fuel data and vehicle mileage.
5. Route Visualization: Polylines are generated and streamed to the frontend via Google Maps JS SDK.

Example Output Payload

{
  "status": "success",
  "data": {
    "ride_id": 104,
    "classification": "Standard Car",
    "route": "SRM Main Gate -> Chennai Central",
    "cost_per_head": 142.50,
    "confidence_level": 0.98
  }
}

Environment & Deployment

Hardware & Engine Requirements

• Python 3.10+ Runtime
• PostgreSQL 15+
• External API Gateways: Google Maps (Directions, Places, Maps)

Infrastructure Initialization

A. Application Service (Flask)

# Initialize environment
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

# Run server
python run.py

Technical Roadmap

• Multi-Agent Orchestration: Implementation of automated pickup scheduling loops to increase detection accuracy.
• SRM/KTR Integration: Direct ingestion of campus event schedules for high-demand ride prediction.

License

Distributed under the MIT License. Professional use only.