Overview
Schiphol Airport, one of the busiest airports in Europe, faced challenges in managing passenger flow effectively in the aftermath of the pandemic. As the Airport Operations Center (APOC) sought to reduce waiting times and ensure smooth operations, they recognized the need for a more accurate and data-driven approach to predicting passenger flows based on historic passenger flows as well as how this causally translates with the increasing demand at airport through the expected flight schedules. We assisted the team at Schiphol to develop an innovative and simple solution to help Schiphol Airport overcome these challenges.
Results
The developed forecasting tool has delivered significant benefits to Schiphol Airport and its passengers. Key results include:
Reduced waiting times: By accurately predicting passenger flows, the airport has been able to allocate resources more efficiently, reducing waiting times and improving the overall travel experience.
Enhanced operational efficiency: The APOC team can now make better-informed decisions, leading to optimized resource allocation and smoother airport operations.
In line with Latitude’s mission, this project has showcased the power of simple and elegant AI-driven solutions in maximizing societal impact and improving the travel experience for millions of passengers at Schiphol Airport.
model accuracy
reduction in wait times
Solution
Together with Schiphol’s team, we worked on an internal tool that forecasts waiting times at the airport by predicting passenger flows based on flight schedules and expected departure times. This tool enables the APOC to proactively allocate resources, including staff and security checkpoints, to accommodate fluctuations in passenger traffic, ensuring a smooth experience for travellers.
Method
To address Schiphol Airport’s challenge of optimizing passenger flow and reducing waiting times, the team began by analyzing historical and real-time data. Based on this analysis, a simulation-driven approach was developed to test different scenarios and understand how resource allocation impacts efficiency across the airport.
Data Analysis
Historical and real-time passenger data were analyzed to identify patterns in passenger flows and operational constraints.
Simulation Modeling
A discrete-event simulation model was built using the SimPy library to explore different passenger flow scenarios.
Scenario Testing
The model allowed the team to simulate counterfactual situations, define causal relationships in passenger flows, and identify bottlenecks.
Deployment & Iteration
After validation, the model was deployed as an internal tool for the Airport Operations Center and is continuously monitored and improved with new features.
Conclusion
Latitude assisted Schiphol Airport in managing passenger flow by developing an AI-powered forecasting tool to predict passenger traffic. The solution, which is based on historical and real-time data, utilises discrete-event and counterfactual simulation to optimise resource allocation strategies and identify bottlenecks. The implementation of this internal tool has significantly reduced waiting times and improved operational efficiency, enhancing the travel experience for millions of passengers at one of Europe’s busiest airports.
