Smart Bus Optimization Challenge

📌 Problem Statement

Urban bus systems in Indian Tier-1 cities (e.g., Bangalore, Delhi, Pune) rely on static timetables that fail to adapt to real-world conditions.
This leads to:

• Bus bunching (multiple buses arriving together)
• Under-utilized trips during off-peak hours
• Unpredictable passenger wait times

Transit agencies lack tools to forecast demand surges and adjust schedules in real time.

Prototype built in 36 hours for Hackathon

Urban bus systems in Tier-1 Indian cities often run on static timetables. This causes bus bunching, empty off-peak trips, and unpredictable wait times.
Our solution: a Smart Bus Management System that adapts in real time to improve efficiency and passenger experience.

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Features

Data ingestion → uses multiple CSVs Real-time simulation → buses move with mocked GPS + live passenger counts
Scheduling engine → reschedules delayed buses, dispatches extras if overcrowded
Prediction model → forecasts ridership for upcoming hours
Alerts → detects delays, overcrowding, and notifies in real time
Dashboard/UI → shows optimized vs original schedules, ridership charts, alerts, and live bus map

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🛠️ Tech Stack

• Backend: FastAPI (Python)
• Frontend: HTML, JavaScript (Chart.js, Leaflet.js)
• Data/ML: Pandas, Scikit-learn / basic time series
• Database: SQLite (for prototype)

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🚀 Quickstart

# 1. Clone repo
git clone https://github.com/your-username/smart-bus-optimization.git
cd smart-bus-optimization

# 2. Create virtual environment
python -m venv venv
source venv/bin/activate   # Mac/Linux
.\venv\Scripts\activate    # Windows

# 3. Install dependencies
pip install -r requirements.txt

# 4. Run backend
uvicorn backend.app:app --reload