Case study · Artificial Intelligence
Airport Passenger Flow Forecasting (Hybrid AI)
A forecasting system for passenger volume across airport zones, supporting better queue and staff management.
LSTM
Deep-learning model
GNN
Graph network
85%
Forecast accuracy
Live
Updated forecasts
Challenge
Operations needed to know several hours ahead how many passengers each key zone would receive, so counters and staff could be arranged correctly and congestion reduced.
Approach
A framework was built to forecast passenger flow across airport zones, combining historical passenger data with deep-learning models.
Our role
01
Data Analysis
Examined historical passenger-flow patterns.
02
Model Design
Tuned LSTM and graph network models to the problem.
03
Implementation
Implemented and evaluated the forecasting framework.
TechnologiesPythonLSTMGNNPandas
