This repository contains a comprehensive analysis of the performance of Hold Baggage Screening X-ray Machines at Terminal 3 during the summer season. The analysis is based on bag throughput data for the month of July, focusing on identifying performance challenges, bottlenecks, and opportunities for optimization.
The analysis is available as an interactive Streamlit app, accessible at:
🔗 Airport_Operations_Analytics
The dataset used for this analysis includes the following key attributes:
- bag_scan_timestamp: Date and time a bag was seen at a machine.
- bag_license_plate: Unique identifier for a bag.
- scan_machine_id: Unique identifier for a machine.
- scan_machine_cluster: The cluster each machine belongs to.
- scan_machine_level: The current screening level the bag is being processed at.
- scan_machine_result: The screening result of the bag at its current screening level.
- scan_machine_result_reason: Further detail on the screening result of the bag.
The analysis addresses several key questions, including:
✅ Throughput and load distribution
✅ Peak days and times for bag screening
✅ System bottlenecks and time-out situations
✅ Machine and cluster utilization
✅ Screening escalations and Level 2 analysis
✅ Single vs. multiple screenings
✅ Decision-making times
✅ Operator interventions
├── Cem_Saydam_Streamlit.py # Main Streamlit app script
├── Xray_Scan_Data_Jul_2022.csv # Dataset used for analysis
├── company_logo.JPG # Company logo used in the app
├── README.md # This file
└── requirements.txt # Dependencies required to run the app
Follow these steps to run the analysis locally:
git clone https://github.com/asayda01/Airport_Operations_Analytics
cd Airport_Operations_Analyticspip install -r requirements.txtstreamlit run streamlit_app.pyOpen your web browser and navigate to: http://localhost:8501
- Daily Throughput: Identifies peak days and overall daily bag processing capacity.
- Hourly Throughput: Highlights the busiest hours for screening.
- 15-Minute Intervals: Detects short-term spikes in activity.
- Determines the busiest days and suggests operator schedule optimization.
- Assesses machine clusters prone to time-outs.
- Identifies peak times for system slowdowns.
- Evaluates equitable distribution of bag processing.
- Identifies machines handling excessive loads and investigates malfunctions.
- Analyzes escalation rates to Level 2 screening.
- Detects trends in escalation over time and across machines.
- Investigates unnecessary bag recirculations.
- Examines causes of redundant screenings.
- Measures the average time operators spend on screening decisions.
- Analyzes time intervals between consecutive bag scans.
- Identifies the percentage of bags requiring manual review.
- Examines intervention trends by machine and time of day.
The app includes interactive visualizations such as:
📊 Bar Charts - Throughput by day, hour, and 15-minute intervals.
📈 Line Charts - Time-based trends in Level 2 escalations and recirculations.
🥧 Pie Charts - Distribution of screening results and operator interventions.
📦 Box Plots - Time spent per bag at each machine.
🔥 Heatmaps - Machine and cluster performance visualization.
Based on the analysis, we provide actionable insights to optimize the screening process:
✅ Optimizing Staffing - Align operator schedules with peak screening times.
✅ Improving Machine Calibration - Reduce false positives and time-outs.
✅ Enhancing Maintenance - Prioritize machines with higher malfunction rates.
✅ Training Operators - Reduce intervention times and increase efficiency.
Contributions are welcome! If you have suggestions or improvements, please open an issue or submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for details.
- [Company Name Removed] - For providing the dataset and supporting this analysis.
- Streamlit - For the framework enabling interactive data exploration.
🔗 Airport_Operations_Analytics
🚀 Thank you for visiting! Feel free to explore, contribute, and improve the analysis!