Inspiration
Many passengers face difficulties during peak hours because buses become overcrowded. People often spend a lot of time waiting for buses without knowing the crowd level. This inspired us to build CrowdSense AI, a smart system that predicts bus crowd levels before travel.
What it does
CrowdSense AI predicts the expected crowd level in a bus based on factors such as time, day, and historical travel patterns. The system helps passengers choose less crowded buses, reduce waiting time, and improve travel comfort.
Key Features:
- Bus crowd prediction
- Crowd percentage estimation
- Crowd status (Low, Medium, High)
- User-friendly web interface
- Smart public transport assistance
How we built it
Frontend:
- HTML
- CSS
- JavaScript
Backend:
- Python Flask
Machine Learning:
- Scikit-learn
Dataset:
- CSV-based historical bus occupancy data
The machine learning model is trained using historical crowd information and predicts future crowd levels based on user inputs.
Challenges we ran into
- Finding suitable public transport datasets
- Preparing and cleaning data
- Building an accurate prediction model
- Integrating the machine learning model with the web application
Accomplishments that we're proud of
- Successfully developed a working AI-based crowd prediction system
- Created a simple and user-friendly web application
- Applied machine learning to solve a real-world smart city problem
What we learned
- Machine Learning model development
- Flask web development
- Data preprocessing and analysis
- Team collaboration and project management
What's next for CrowdSense AI
- Real-time GPS integration
- Live bus tracking
- Weather-based prediction
- Mobile application support
- Smart city transport dashboard
Built With
- artificial-intelligence
- bootstrap
- css
- data-science
- database
- decision-tree
- flask
- github
- html
- javascript
- machine-learning
- numpy
- pandas
- predictive-analytics
- python
- random-forest
- rest-api
- scikit-learn
- sqlite
- ui/ux
- web-application
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