Benefitting from Heat Map Analysis in Airport Terminals
Challenge
A growing focus on data-driven spatial awareness as part of capacity-maximization and experience-enhancement strategies has airports globally looking for tools that can help them understand how each square inch of terminal space is used. The topic is increasingly important for operators that need to know if excessive and unstructured queues are the result of overcapacity or underutilization of infrastructure resources. Optimizing resource space and improving passenger flow throughout the terminal requires precise data about:
Which areas of the terminal see the highest or lowest foot traffic?
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What is the impact of layout changes or upstream/downstream operational adjustments?
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Failing to synthesize product choice and positioning with customer demands
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Solution
Heat maps—measuring the density of passenger flow in designated terminal spaces—help solve terminal operators’ different objectives. Frequency, Total Dwell Time, and Average Dwell Time are the three most useful types of heat maps in the airport terminal management space.
A powerful visual tool for identifying high-traffic zones and peak times, heat maps provide insights in a blink that operators can use to:
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Identify: areas with the highest incident of clustering or potential for concessionaires
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Test: impact of signage repositioning, staff allocation, or service counters
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Staff exclusion, which improves the accuracy of conversion rate measuring
Upgrading from Xovis' on-premises solution to the managed AERO service will help Carrasco International Airport reduce wait times and improve passenger throughput.
Benefits
Heat mapping helps airports create better situational awareness throughout the terminal space, which is crucial for:
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Enhancing passenger flow by positioning critical services in the right areas
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Reducing operating costs by optimizing staff deployment based on demand
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Improving passenger experience by minimizing congestion in security and check-in lines
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Maximizing terminal infrastructure through data-driven planning and scheduling
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