The University Bus Routing and Scheduling System is designed to improve the efficiency of the University of Vavuniya's bus system by leveraging advanced algorithms and machine learning techniques. This project aims to optimize bus routes and schedules to reduce travel time and overcrowding, and implement a dynamic system that adapts to real-time changes. A user-friendly web application will be provided for students to access updated bus schedules and routes.

1. Introduction
2. Problem Identification
3. Requirements Collection
4. Algorithm Design
5. Space and Time Complexity Analysis
6. Proposed AI/ML/DL Algorithm for Efficiency Improvement
7. Comparison of Basic and Intelligent Algorithms
8. Conclusion
9. Contributions
10. Setup Requirements and Instructions

A University Bus Routing and Scheduling System is designed to efficiently manage the transportation needs of students, faculty, and staff within a university. This system ensures that buses are routed and scheduled in a way that maximizes convenience and minimizes travel time, while also considering cost-effectiveness and operational efficiency.

The University of Vavuniya currently faces several challenges with its bus transportation system:

• Inefficient routes leading to increased travel times.
• Inconsistent bus schedules causing delays and missed connections.
• Insufficient coverage of high-demand areas.
• Poor real-time tracking and lack of dynamic adjustments.

To address the identified problems, the following requirements were gathered through surveys and stakeholder meetings:

• Efficient route planning to cover all essential locations.
• Timetable creation that balances frequency and demand.
• Real-time bus tracking and dynamic scheduling adjustments.
• Cost-effective resource allocation including bus fleet and driver management.

1. Route Planning: Map all critical locations within and around the university.
2. Scheduling: Develop timetables considering peak and off-peak times.
3. Real-Time Monitoring: Implement GPS and tracking systems.
4. Resource Allocation: Optimize bus fleet and driver schedules to ensure coverage and compliance.

1. Identify all stops and map routes covering essential locations.
2. Use historical data to estimate demand for each stop.
3. Create initial timetables with more frequent services during peak hours.
4. Allocate buses and drivers based on the timetables and expected demand.
5. Implement GPS tracking on all buses for real-time monitoring.

• Route Mapping: (O(N^2)) where (N) is the number of stops.
• Timetable Storage: (O(T)) where (T) is the number of time slots in the schedule.
• GPS Data: (O(B)) where (B) is the number of buses.

• Route Planning: (O(N^2)) due to mapping between stops.
• Timetable Creation: (O(N \cdot T)) for creating schedules for each stop and time slot.
• Real-Time Adjustments: (O(1)) for each adjustment, assuming efficient data handling.

We propose using a Reinforcement Learning (RL) algorithm to optimize bus routes and schedules dynamically.

1. Define the state space including current bus locations, passenger demand, and traffic conditions.
2. Define actions as route changes, schedule adjustments, and bus dispatches.
3. Implement a reward system based on minimizing travel time and maximizing coverage.
4. Use Q-Learning to train the model, allowing the system to learn optimal actions over time.

• Efficiency: Limited to predefined routes and schedules.
• Adaptability: Static, requires manual adjustments for changes.
• Cost: Lower initial cost but higher long-term operational inefficiencies.

• Efficiency: Learns and adapts to optimize routes and schedules dynamically.
• Adaptability: High, adjusts in real-time to changes in demand and traffic.
• Cost: Higher initial cost for development and training but lower operational costs due to improved efficiency.

Implementing a University Bus Routing and Scheduling System at the University of Vavuniya can significantly improve transportation efficiency, reduce travel time, and enhance user convenience. By leveraging AI and machine learning techniques, the system can dynamically adapt to changing conditions, providing a robust and cost-effective solution.

• Problem Identification: V. Harson (2020/ICT/59)
• Requirements Collection: S. Venuka (2020/ICT/70)
• Algorithm Design: K. R. Insafa (2020/ICT/80)
• Complexity Analysis: S. F. Sumaiya (2020/ICT/62)
• AI/ML/DL Proposal: C. V. Sushmitha (2020/ICT/53)
• Comparison Analysis: M. J. Ruzla (2020/ICT/82)

• XAMPP installed on your machine.
• Basic knowledge of using XAMPP and web servers.

1. Clone the repository

   git clone https://github.com/hrshdeve/university-bus-routing.git
   cd university-bus-routing

2. Move project files to XAMPP's web server directory

   • Open your XAMPP installation directory (e.g., C:\xampp on Windows or /Applications/XAMPP on macOS).
   • Navigate to the htdocs directory inside XAMPP (C:\xampp\htdocs on Windows or /Applications/XAMPP/htdocs on macOS).
   • Copy or move the university-bus-routing directory into htdocs.

3. Start Apache server in XAMPP

   • Open XAMPP Control Panel.
   • Start Apache by clicking the "Start" button next to Apache in the control panel. This will start the Apache web server.

4. Access the application

   • Open a web browser.
   • Go to http://localhost/university-bus-routing. This URL assumes that university-bus-routing is the name of the directory you copied into htdocs.

• Server Configuration: XAMPP's Apache server configuration is typically done through httpd.conf located in C:\xampp\apache\conf on Windows or /Applications/XAMPP/xamppfiles/apache2/conf on macOS.
• Environment Variables: If your application requires any environment variables (like database credentials or API keys), you may set them in your php.ini or use .env files if your application supports it.

• For local development and testing, XAMPP provides an easy environment to run PHP applications like this one. Ensure all dependencies are met and configurations are correctly set up.

• Open your web browser and visit http://localhost/university-bus-routing to test the application.
• Verify that all pages load correctly and functionality (such as links and forms) works as expected.

This README provides an overview and setup instructions for the University Bus Routing and Scheduling System. For any issues or contributions, please refer to the CONTRIBUTING.md file.