Airport Flow AI Predictor
Indira Gandhi International Airport (IGIA)
Live
⚡ 95% Accurate
✈
AI Powered Resource Management
Airport AI Future Predictor
Resource Management System
Resource Management System
AI-powered system using Random Forest ML to predict hourly passenger flow at Indira Gandhi International Airport — enabling proactive allocation of check-in counters, security staff, and boarding gates to minimize wait times and maximize operational efficiency.
95%
Accuracy
4,368
Records
0.95
R² Score
👥
Peak
491
Peak Passengers / Hour
Morning rush 6–9 AM
🪟
Check-in
8
Max Counters Open
During peak hours
👮
Security
16
Max Security Staff
Guards at peak hours
🚪
Boarding
4
Max Gates Active
Boarding gates open
✈ Predicted Passengers
490
passengers / hour
🔴 Peak Hour
🪟 Counters Needed
8
check-in counters
Open now
👮 Security Staff
16
guards required
On duty
🚪 Gates to Open
4
boarding gates
Active
Passenger Flow — 24 Hours
AI predicted hourly passenger count
Peak
Normal
Counters Required / Hour
Check-in counter allocation
👮 Security Staff / Hour
🚪 Gates / Hour
📊 Peak vs Off-Peak
AI Prediction Engine
Hourly Passenger Predictions
ML model predictions for all 24 hours at IGIA — weekday vs weekend comparison with complete resource requirements per hour.
491
Max Passengers
52
Min Passengers
8
Peak Hours
🌅 Morning & Evening Peak Hours
| Hour | Passengers | Counters | Staff | Status |
|---|---|---|---|---|
| 06:00 | 491 | 8 | 16 | Peak |
| 07:00 | 488 | 8 | 16 | Peak |
| 08:00 | 490 | 8 | 16 | Peak |
| 09:00 | 485 | 8 | 16 | Peak |
| 17:00 | 440 | 8 | 14 | Peak |
| 18:00 | 444 | 8 | 14 | Peak |
| 19:00 | 450 | 8 | 15 | Peak |
| 20:00 | 446 | 8 | 14 | Peak |
🌙 Off-Peak Hours
| Hour | Passengers | Counters | Staff | Status |
|---|---|---|---|---|
| 00:00 | 52 | 2 | 3 | Off-Peak |
| 01:00 | 55 | 2 | 3 | Off-Peak |
| 02:00 | 54 | 2 | 3 | Off-Peak |
| 03:00 | 57 | 2 | 3 | Off-Peak |
| 10:00 | 168 | 3 | 5 | Off-Peak |
| 11:00 | 248 | 4 | 8 | Off-Peak |
| 15:00 | 162 | 2 | 5 | Off-Peak |
| 23:00 | 167 | 3 | 5 | Off-Peak |
Complete 24-Hour Prediction Chart
All hours — weekday predictions
Resource Allocation Engine
Smart Resource Management
AI-based recommendations for IGIA operations — check-in counters, security staff, and boarding gates optimized for each hour to reduce passenger wait time and improve airport efficiency.
8
Max Counters
16
Max Staff
4
Max Gates
🪟 Check-in Counters by Hour
👮 Security Staff by Hour
🚪 Gates to Open by Hour
Resource Comparison — All Types
Resource Distribution
Model Performance Analytics
Deep Analytics & Insights
Complete model performance metrics, seasonal patterns, data statistics, and key operational insights from the AI prediction system built for Indira Gandhi International Airport.
0.95
R² Score
30.67
MAE
6 mo
Data Period
4,368
Total Records
16
Features Used
80/20
Train/Test Split
100
Trees (Forest)
256
Avg Passengers
33%
Peak Hour %
📊 Monthly Passenger Trend
🤖 Model Performance Metrics
💡 Key Insights from AI Model
Morning Peak (6–9 AM): Highest passenger volume of the day — 491 passengers/hour. Requires 8 counters, 16 security staff, and 4 gates open simultaneously.
Evening Peak (5–8 PM): Second peak with ~440–450 passengers/hour. Weekend evenings show 25% higher traffic compared to weekdays.
Night Hours (0–4 AM): Minimal traffic with only 52–57 passengers/hour. Minimum resources needed — 2 counters, 3 staff, 1 gate.
Holiday Impact: Holiday periods show 45% surge in passenger traffic. Pre-holiday alerts allow proactive resource deployment.
Model Accuracy: Random Forest model achieves 95% accuracy (R²=0.95) with MAE of just 30.67 passengers — highly reliable for operational planning.
Documentation
System Docs & API Guide
Complete documentation for Airport AI Predictor — ML model details, API references, integration guides, and operational manuals for IGIA resource management system.
v2.0
Version
RF
Algorithm
REST
API Type
📘 Model Documentation
Algorithm: Random Forest Regressor with 100 decision trees. Trained on 4,368 historical passenger records from IGIA Delhi.
Features: 16 input features including hour, day of week, season, holiday flag, terminal, flight type, and weather index.
Performance: R²=0.95, MAE=30.67 passengers/hr, RMSE=41.2. Validated on 20% holdout test set with consistent accuracy.
Update Cycle: Model is retrained monthly with new data. Live predictions refresh every 15 minutes via scheduled inference pipeline.
🔌 API Reference
GET /api/predict?hour={h} — Returns predicted passenger count, counters, staff, and gates for a given hour (0–23).
GET /api/forecast/24h — Full 24-hour forecast with all resource allocations. Response time <200ms.
POST /api/alert — Trigger custom threshold alerts. Supports email, SMS, and webhook notifications for ops team.
Auth: Bearer token required. Rate limit: 1000 requests/hour per token. Contact admin for API key provisioning.
Configuration
System Settings
Configure dashboard preferences, notification thresholds, display options, and resource allocation limits for the Airport AI Predictor system.
IGIA
Airport
DEL
IATA Code
IST
Timezone
🖥️ Display Settings
Time-Based Theme: Auto-switches background based on selected hour — sky blue morning (6–11), sunny afternoon (12–19), dark night (19–5).
Refresh Rate: Live data updates every 15 minutes. Manual refresh available via header button. Charts animate on data change.
Responsive Layout: Sidebar on desktop/tablet, bottom navigation on mobile. Automatically adapts to screen width.
🔔 Alert Thresholds
Peak Alert: Triggered when predicted passengers exceed 400/hr. Notifies ops team 30 minutes in advance for resource prep.
Moderate Alert: 250–400 passengers/hr. Suggests opening additional counters and pre-positioning staff near gates.
Low Traffic: Below 150 passengers/hr. Minimum resource mode — 2 counters, 3 security staff, 1 active gate.