Hungary’s Budapest Ferenc Liszt International Airport has announced it is to implement Veovo’s passenger flow analytics platform, Passenger Predictability.
Veovo’s cloud platform is scalable and enables initial implementation at the optimal point of impact, expanding to include measurement, prediction and planning in other areas as they are identified. For example, Budapest Airport will begin using the technology at security and check-in and plans to use it to forecast passenger show-up profiles and build efficient lane opening plans to match demand.
The platform is intended to improve the passenger experience and the airport’s operations. It will initially focus on preventing bottlenecks and efficiently allocating resources in the airport’s security and check-in areas. With an expected influx of travelers in the summer, Budapest Airport intends to use data from multiple movement sensors and machine learning to understand passenger behavior, predict the impact of events and make informed proactive decisions.
The airport also plans to expand the technology from curb to gate to benefit passengers throughout their airport journey. Live and predicted queue wait-time information will also be displayed to provide accurate information to passengers about the expected queueing time, reducing stress and anxiety and helping them take control of their journey.
István Szabó, chief passenger services director at Budapest Airport, said, “Our commitment is to deliver world-class service to our customers and continuously enhance our operational processes. Our collaboration with Veovo as a technology partner is a vital aspect of this strategy, enabling us to gain valuable insights, optimize staffing, and improve experiences at our airport checkpoints.”
James Williamson, CEO at Veovo, commented, “We’re delighted to work with Budapest Airport to help improve operations and streamline passenger journeys. The airport is one of a growing number of airports tapping into the power of data and machine learning to transform their operational performance.”
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