🚢 Ferry Operations Intelligence Dashboard
A professional-grade Streamlit analytics dashboard built for monitoring and analyzing ferry ticket operations from 2015–2025 using interactive visualizations, KPI tracking, congestion detection, and operational efficiency analytics.
This project transforms raw ferry ticket transaction data into actionable operational intelligence using Python, Streamlit, Plotly, Pandas, and NumPy.
📌 Features 📊 Advanced KPI Monitoring
The dashboard calculates and visualizes operational KPIs such as:
Capacity Utilisation Ratio (CUR) Congestion Pressure Index (CPI) Idle Capacity Percentage (ICP) Peak Strain Duration (PSD) Operational Variability Score (OVS) 📈 Temporal Analytics
Analyze ferry activity across:
Hourly traffic patterns Daily rolling averages Monthly activity distribution Year-over-year operational trends 🔥 Congestion & Idle Detection
Detect operational stress using:
Operational Load Index (OLI) Congestion thresholds Idle interval analysis Monthly congestion trend monitoring 📅 Segmentation Analysis
Break down operational efficiency by:
Weekday vs Weekend Seasonal performance Shift-level utilization Hour × Day heatmaps 🧹 Data Quality Monitoring
Built-in diagnostics include:
Missing interval detection Zero-activity tracking Negative anomaly detection Descriptive statistics analysis 🛠️ Tech Stack Technology Purpose Python Core programming Streamlit Dashboard framework Plotly Interactive visualizations Pandas Data manipulation NumPy Numerical operations 📂 Project Structure ├── streamlit.py ├── Ferry tickets.csv ├── requirements.txt └── README.md ⚙️ Installation 1️⃣ Clone the Repository git clone https://github.com/your-username/ferry-operations-dashboard.git cd ferry-operations-dashboard 2️⃣ Install Dependencies pip install -r requirements.txt 3️⃣ Run the Application streamlit run streamlit.py 📦 Required Libraries
Create a requirements.txt file with:
streamlit pandas numpy plotly 📁 Dataset Requirements
The dashboard expects a CSV file named:
Ferry tickets.csv
Required columns:
Column Name Description Timestamp Date & time of transaction Sales Count Number of ticket sales Redemption Count Number of ticket redemptions 🧠 Feature Engineering
The project automatically generates:
Date & time features Shift categorization Seasonal classification Operational Load Index (OLI) Congestion flags Idle flags Rolling activity metrics Redemption pressure metrics 🎨 UI Highlights Dark futuristic theme Interactive Plotly charts Dynamic filtering sidebar Responsive layout KPI cards with color indicators Multi-tab analytical structure 📊 Dashboard Sections 📈 Temporal Patterns Hourly activity profile Daily smoothing trends Monthly distribution analysis 🔥 Congestion & Idle OLI distribution Congestion vs idle intervals Monthly pressure trends 📅 Segmentation Seasonal efficiency Shift analysis Weekday/weekend comparison Activity heatmaps 📊 Trend Analysis Annual KPI monitoring Sales vs redemption trends Operational variability tracking 🔍 Data Quality Missing intervals Null checks Anomaly identification Statistical summaries 🚀 Future Improvements
Potential upgrades:
Machine Learning demand forecasting Real-time API integration Predictive congestion alerts Passenger flow optimization Database integration (PostgreSQL/MySQL) Authentication system Cloud deployment 📸 Preview
Add screenshots or GIFs here after deployment.
Example:
🌐 Deployment Options
You can deploy this dashboard on:
Streamlit Community Cloud Render Railway Hugging Face Spaces