top of page

CASE STUDY. ZURICH AIRPORT PASSENGER FLOW TOOL

Objective.

Optimize passenger flows, reduce wait times, and improve workforce scheduling at Zurich Airport.

Solution.

Utilized a deep learning forecasting engine to analyze and predict passenger movements and behavior patterns.

Achievements

  • Increased turnover by 21%

  • Boosted non-aviation revenue by 18%

  • Significantly improved passenger satisfaction (1% customer satisfaction = 1.5% non aeronautical revenue increase)

Collaborators.

Lufthansa, SUPSI, HSLU, Databoosters, BEDA, STI Foundation, Xovis, Zurich Airport

Summary.

The Research & implementation of the Passenger Flow algorithms at Zurich Airport demonstrated the power of predictive analytics in enhancing airport operations. By leveraging deep learning, SWIXS helped Zurich Airport streamline passenger flow, resulting in increased revenue and higher satisfaction rates. This project exemplifies how innovative solutions can transform airport management and improve the overall travel experience.

WHAT

Passenger Forecasting 

WHERE

Zurich Airport

WHEN

March 2022 -June 2023

bottom of page