The Airport’s Unsolved Problem

Though the aviation industry solved many friction points at the passenger terminal, one part of the journey remains stubbornly unreformed. The security checkpoint continues to be one of the most frustrating touchpoints in the airport journey for a significant share of travellers. The Aviation Cooperative Research Program (ACRP) has commissioned a study to develop a primer, guide, and tools — including a decision tree, flow chart, and scorecard — to help airport practitioners evaluate and implement virtual queuing solutions for managing demand at the security screening checkpoint. The outcome of the study is not yet publicly released, though the industry eagerly awaits the guide.

The economics of this frustration are real. Passengers stuck in long security queues arrive at the gate with less discretionary time and less money spent on retail and F&B. Airport concession revenues are directly correlated with passenger dwell time in the secure zone; research consistently indicates that every additional minute of secure dwell time generates meaningful incremental spend per passenger.

Virtual queuing offers a credible path to disrupting this dynamic. The concept is straightforward: rather than requiring physical presence in a line, passengers are assigned a time window for checkpoint processing. The queue still exists, but it is virtual — not concentrated at a single pinch point.

Learning From Adjacent Sectors

Virtual queuing is not a new idea. Disney introduced FastPass in 1999, a system that has since evolved into a fully dynamic demand management platform. Healthcare systems use appointment scheduling — a form of virtual queuing — to manage patient flow. Retailers from Apple to IKEA have deployed queue management systems that replace physical waiting with mobile notifications. The concept has proven itself across industries where demand is concentrated, capacity is constrained, and the customer experience consequence of waiting is high. Airports meet all three conditions.

The more instructive examples are from inside the airport perimeter itself. Concessionaires and service providers at major airports have begun deploying virtual queuing for lounge access, premium services, and customer support. Several international airports — particularly in the Middle East and Europe — have piloted appointment-based security systems for priority lanes. The Automated Passport Control and CBP Mobile Passport systems at US ports of entry represent early steps toward demand-managed processing at border checkpoints. The infrastructure and behavioural models are converging.

Designing for the Checkpoint Environment

Translating virtual queuing from adjacent sectors to the security checkpoint requires navigating a set of constraints that have no direct equivalent in retail or theme parks.

The physical geometry of security checkpoints is non-trivial. Pre-screening and screening areas at most airports were designed for throughput optimisation under a first-come, first-served model, not demand-managed flow. A virtual queuing system must be designed around the specific layout of each airport terminal — the available holding space, the number of lanes, the position of WTMD and CT scanner banks, and the physical flow from check-in through to the secure zone. Airports with centralised security zones face different design challenges than those with distributed checkpoint clusters across multiple concourses.

Sample departing seats rolling hour

Demand profiles matter enormously. A virtual queuing system that works well during predictable peak periods — Monday morning business travel at a hub airport — will face harder conditions during irregular operations, weather events, or special events that compress demand into unexpected patterns. The system must account simultaneously for diurnal patterns and passenger profile: the frequent flyer who manages the slot to the minute, and the infrequent traveller who arrives at the wrong time regardless of what the app instructs.

An example of a demand rolling hour from a predominantly Origin-Destination (O-D) airport in India illustrates the challenge clearly. The rate of change matters as much as the peak itself: the system can go from near dormancy to maximum load in under two hours — a demand profile typical of many business-travel-dominated airports globally. A virtual queuing algorithm that sets slot intervals based on yesterday’s throughput rates could cause a mismatch between predicted and actual arrival rates, and the consequences — a queue forming faster than the system can adapt — are precisely what the system was designed to prevent.

The downstream and upstream interplay is also critical. The security checkpoint does not sit in isolation. Its throughput is constrained by arrivals from check-in and the upstream flow from ground transportation, and it drives what arrives at gates and in concession areas. A virtual queuing system that smooths checkpoint demand without coordinating with upstream passenger arrival patterns will create new bottlenecks rather than eliminate existing ones. The most effective implementations will be designed as components of a broader demand management architecture, not standalone point solutions.

The passenger-facing interface is the most visible component, but it sits above a more complex infrastructure layer. An effective virtual queuing system requires real-time processing rate data to assign realistic time slots, dynamic adjustment capability as conditions change, integration with airline departure data to prevent slot assignments that conflict with boarding close-out times, and a passenger notification system capable of reaching travellers across a range of devices and engagement behaviours.

The Adoption Hurdle

The adoption problem is arguably harder than the algorithm problem. A virtual queuing system that 60 percent of departing passengers ignore does not manage demand — it creates a two-tier queue where the compliant minority holds slots while the non-compliant majority walks up and forms a conventional line anyway, destroying the throughput model the system was designed around.

The single most important design decision is where in the journey enrolment is solicited. Most pilot deployments have placed the sign-up moment at the airport — a QR code at the terminal entrance, a kiosk near the checkpoint, a push notification triggered by geofence. By the time a passenger is at the terminal, their luggage is in hand and cognitive bandwidth is at its lowest. Enrolment rates at terminal touchpoints consistently underperform pre-journey channels by a large margin.

The correct enrolment window is the 24-to-48-hour pre-departure period, embedded inside processes the passenger is already completing. The check-in confirmation screen — the moment the passenger receives their boarding pass — carries high attention. A single-screen slot selection prompt inserted here, requiring one tap to accept a suggested window or two taps to choose an alternative, captures the passenger at peak engagement. Airlines control this touchpoint entirely; the airport’s integration requirement is an API that accepts a PNR and returns an available slot window.

Airport app enrolment has a structural limitation: install rates for standalone airport apps are low relative to airline app penetration, concentrated among frequent business travellers, and drop sharply for passengers originating from outside the primary catchment. The airport app’s structural advantage is that it is not airline-specific — a passenger with a connecting itinerary involving two airlines, or a family group with split bookings, can consolidate their virtual queue management on a single interface. The cleanest mechanism mirrors what the check-in flow does in the airline app: it fires the slot assignment process the moment a boarding pass is detected, requesting a slot and displaying the recommended window with a one-tap confirm.

Case Study: PreSecure at Bengaluru’s Kempegowda International Airport

Recently, Bangalore International Airport Limited (BIAL) became the first Indian airport to operationalise a virtual queue system for security screening. The pilot, branded PreSecure, runs at Terminal 1 through the BLR Pulse app and has delivered the sector’s most instructive Indian data point on adoption mechanics and operational design.

Passengers open the BLR Pulse app, scan their boarding pass, and select an available security slot from a menu of time windows. The booking window closes 75 minutes before departure — tight enough to capture passengers in a useful planning state, but sufficient to give the system demand data ahead of the actual peak. Passengers who book through PreSecure are directed to a dedicated security lane, physically separated from the standard queue.

The structural strength of PreSecure is its direct boarding pass integration. Unlike systems that require passengers to create a separate virtual queue account, the BLR Pulse flow anchors enrolment to an artefact the passenger already holds. This removes a cognitive barrier that has historically suppressed adoption in European pilots.

The structural limitation is equally visible: PreSecure’s adoption ceiling is tied to BLR Pulse install rates, which — consistent with the airport app penetration data discussed above — skews heavily toward frequent business travellers. Infrequent flyers departing from Terminal 1, who may be travelling for the first time in twelve months, are the least likely to have BLR Pulse installed and the most likely to create unpredictable queue demand. This is the core tension in every virtual queue deployment: the system’s value accrues disproportionately to the passengers who are already easiest to serve.

BIAL has confirmed it is evaluating a paid model once the trial phase concludes, and an expansion to Terminal 2 — which handles international and select additional domestic operations — is under consideration.

Avinia’s assessment is that PreSecure represents the right pilot design for the Indian context. The next phase will determine whether BIAL can extend PreSecure’s reach into the infrequent traveller segment — where virtual queuing’s transformative potential lies — and whether a pricing model can generate the commercial return needed to justify infrastructure investment at scale. Bengaluru’s pilot is the most relevant reference point in the Indian market. Every major Indian airport with a congested security checkpoint should be studying its operational data closely.

Freemium, or Priced?

The case for a free offering is strong on passenger experience logic: if virtual queuing meaningfully reduces checkpoint friction, the benefit should flow to the broadest possible base. Congestion pricing — charging more to access a constrained resource during peak demand, to shift discretionary demand to off-peak periods and fund capacity investment from the revenue generated — is a win for the airport.

A hybrid model is possible: a free virtual queuing tier with optional premium windows for passengers willing to pay for greater certainty. The baseline virtual queue system assigns a slot; the paid upgrade lets the passenger select any available window that suits their schedule. This requires sophisticated algorithms and real-time data.

Avinia’s View

Busy airports could gain a durable advantage both in passenger experience performance and in the commercial metrics that drive long-term revenue. The key is not to overcomplicate the technology or the pricing model. The pricing architecture must be designed to serve the system’s primary purpose — demand smoothing — not to maximise revenue at the expense of the experience it is meant to improve. Start with a free, universally accessible offering at a single terminal or checkpoint zone. Measure throughput, dwell time, and satisfaction rigorously. Build the relationship before the technology, not after. Use the data to make the case for broader rollout. The ACRP guidebook, currently in preparation, could be a useful implementation guide for pilot rollout. The moment to run that pilot is now — and Bengaluru has shown the path.