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The Roas project aims to revolutionize the operational efficiency of the Delhi Transport Corporation (DTC) through an Automated Bus Scheduling and Route Management System. Currently, DTC faces challenges due to manual scheduling, inefficient route planning, and delayed emergency responses.

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Roas – Automated Bus Scheduling & Route Management for DTC

Roas aims to revolutionize the operational efficiency of the Delhi Transport Corporation (DTC) through an AI-powered, automated scheduling and route optimization system. By addressing the current challenges of manual operations, Roas provides a smart, scalable, and efficient solution to modernize public transportation in Delhi.


🚀 Project Overview

The Delhi Transport Corporation currently faces critical issues such as:

  • Manual and error-prone scheduling.
  • Inefficient route planning.
  • Delayed emergency responses.

Roas addresses these with a centralized, intelligent system leveraging Artificial Intelligence (AI), Machine Learning (ML), real-time data analytics, and Geographic Information Systems (GIS).


🔍 Key Features

✅ Automated Bus & Staff Scheduling

  • Dynamic assignment of buses, drivers, and staff.
  • Takes into account driver comfort, rest periods, historical data, and shift balancing.
  • Reduces human error and enhances operational reliability.

🚏 Optimized Route Planning

  • Uses ML and GIS to identify the best routes in real-time.
  • Considers traffic, road quality, peak hours, and substations.
  • Continuously adapts to real-world changes for minimum congestion and delay.

🚨 Emergency Response System

  • Automatically detects and manages emergencies like breakdowns.
  • Notifies relevant staff (e.g., mechanics) instantly.
  • Dynamically reroutes buses and resources to maintain service flow.

📊 Manager Dashboard

  • Web-based platform to:
    • Monitor live bus locations.
    • View staff assignments and schedules.
    • Manage emergency requests.
    • Generate analytical reports for performance and planning.

📱 Driver & Staff Mobile App

  • Real-time schedule updates, route info, and colleague coordination.
  • Emergency reporting interface for quick alerts.
  • Enhances communication and responsiveness on the ground.

🛠️ Technologies Used

  • Frontend: HTML, CSS, JavaScript (within Templates and static)
  • Backend: Python, Django Framework
  • Database: SQLite3 (db.sqlite3)
  • AI/ML: For scheduling and route optimization
  • GIS Integration: For geospatial routing
  • Real-Time Data Processing: Via APIs and live traffic inputs

🧠 Innovation and Uniqueness

Roas integrates:

  • AI-driven predictive modeling for staff and route scheduling.
  • Real-time adaptive algorithms for route finding (Raas engine).
  • Emergency automation system with minimal human intervention.
  • Scalable cloud architecture designed to handle large volumes of data and real-time interactions.

This solution provides continuous optimization, setting it apart from traditional static scheduling systems.


📈 How Roas Solves DTC's Challenges

Problem Roas Solution
Manual scheduling errors AI-based automated staff and vehicle scheduling
Inefficient routing ML-based route optimization with real-time data
Emergency response delays Automated alerts and rerouting using predefined protocols
Lack of transparency Live dashboard and staff-facing applications
Scalability issues Cloud-ready, modular architecture for future growth

📢 Conclusion

Roas is a next-generation transport management system tailored to meet the needs of one of India's largest public transport providers. By integrating cutting-edge technologies with user-centric design, it promises to deliver efficiency, reliability, and intelligence at scale.

Empowering DTC to serve millions with smarter, faster, and more reliable transit solutions.

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The Roas project aims to revolutionize the operational efficiency of the Delhi Transport Corporation (DTC) through an Automated Bus Scheduling and Route Management System. Currently, DTC faces challenges due to manual scheduling, inefficient route planning, and delayed emergency responses.

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