Airport & Crowd Flow Analytics Glossary

GLOSSARY TERMS

A

Airside

Airside is the portion of an airport that is accessible only to passengers who have cleared security screening and to credentialed airport and airline staff. It includes departure gates, concourses, taxiways, aprons, and aircraft stands. Airside passenger flow analytics focuses on post-security movement, including transit between gates, retail dwell time, and boarding gate congestion. Monitoring airside traffic requires sensor technologies that can operate in controlled areas under GDPR and passenger privacy requirements, making anonymous 3D LiDAR well suited for airside deployments.

Anonymous Tracking

Anonymous tracking is the practice of measuring and following individual movement paths through a space without capturing or storing any personally identifiable information. In airport analytics, anonymous tracking means counting and routing passengers without using facial recognition, video images, biometrics, or device identifiers. LiDAR-based tracking is inherently anonymous: the sensor measures 3D shape and position in space, producing no images and capturing no biometric data. Quanergy Q-TRACK provides anonymous tracking compliant with GDPR and international data protection standards, making it deployable in public airport terminals without consent workflow requirements.

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B

Baggage Claim Analytics

Baggage claim analytics measures passenger arrival rates, wait times, and crowd density in the baggage claim area after landing. Key metrics include the time between aircraft arrival and first bag delivery, the distribution of passengers around carousels, and average wait time before baggage collection. High crowd density at baggage claim can indicate carousel assignment problems, delayed bag delivery, or insufficient space allocation. Real-time analytics allow airport operators to proactively communicate delays and manage passenger flow between arrival gates and carousels.

Boarding Gate Analytics

Boarding gate analytics measures passenger accumulation, queue formation, and throughput at departure gate areas. Key metrics include the number of passengers waiting at the gate relative to aircraft capacity, the distribution of arrivals over the gate dwell period, and the flow rate through the boarding door during active boarding. Boarding gate analytics help airlines and airports identify early and late boarding patterns, optimize boarding sequence management, and plan gate staffing.

Bottleneck Detection

Bottleneck detection identifies locations within an airport terminal where passenger flow is impeded, causing queue buildup, reduced Level of Service, or congestion spreading into adjacent areas. Common bottleneck points include security checkpoint lanes, passport control counters, narrow corridor junctions, and escalator approaches. Real-time bottleneck detection enables airport operations centers to respond dynamically by opening additional lanes, redirecting passengers, or deploying additional staff before a situation escalates.

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C

Checkpoint Analytics

Checkpoint analytics measures the performance of security screening, passport control, or customs processing points, including queue length, wait time, processing rate per lane, and Level of Service. Real-time checkpoint data allows airport security management to adjust lane allocation, prioritize specific passenger categories, and communicate expected wait times to arriving passengers. Quanergy Q-INSIGHTS provides checkpoint analytics using anonymous 3D LiDAR sensors positioned to measure queue formation and throughput without visual surveillance of individual passengers.

Crowd Density

Crowd density is the number of people occupying a defined area, typically expressed as people per square meter. IATA LOS standards use crowd density thresholds to classify terminal conditions from A (less than 1.2 m² per person, free flow) through F (under 0.5 m² per person, crowd breakdown). Real-time crowd density measurement requires continuous, area-wide people detection and is one of the primary outputs of LiDAR-based passenger flow analytics systems. Quanergy Q-INSIGHTS measures real-time density across defined terminal zones and triggers alerts when density exceeds configured thresholds.

Curbside Monitoring

Curbside monitoring tracks vehicle and passenger activity at the airport’s landside drop-off and pick-up areas, including vehicle dwell time, pedestrian crossing patterns, and curb utilization rates. Excessive vehicle dwell time causes congestion that affects passenger safety and airport access. Real-time curbside data allows traffic management teams to enforce dwell limits and optimize pick-up zone allocation. 3D LiDAR can monitor curbside areas anonymously, distinguishing pedestrians from vehicles and measuring dwell time without cameras.

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D

Density Mapping

Density mapping produces a spatial visualization of crowd concentration across a defined area, showing where people are concentrated at high density and where space is underutilized. A real-time density map updates continuously as passengers move through the terminal, providing airport operations teams with a live picture of crowding conditions across the whole terminal from a single dashboard view. Density maps are generated by aggregating the occupancy data from distributed LiDAR or people-counting sensors into a unified spatial grid.

Dwell Time (Airport)

Dwell time in airport analytics is the total time a passenger spends in a specific zone or area before moving on. Dwell time is measured at check-in, security queues, retail zones, gate areas, and baggage claim. In commercial planning, higher retail dwell time correlates with higher spend per passenger. In operations planning, excessive dwell time at processing points indicates capacity or staffing problems. Quanergy Q-INSIGHTS measures dwell time anonymously by zone using 3D LiDAR tracking, with no requirement for passengers to interact with or opt into any system.

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F

Flow Rate

Flow rate is the number of passengers passing a defined measurement point per unit time, typically expressed as passengers per minute or passengers per hour. Flow rate measurement is a fundamental input to checkpoint and corridor capacity analysis. Comparing actual flow rate against designed capacity reveals whether a given facility can handle peak demand without Level of Service degradation. 3D LiDAR bidirectional counting sensors mounted at doorways, corridor junctions, and checkpoint entries provide continuous flow rate data.

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G

Gate Utilization

Gate utilization measures how effectively individual departure gates are being used relative to their capacity and the available schedule slots. Low gate utilization may indicate scheduling inefficiencies, while consistently overloaded gates point to capacity constraints. Real-time gate utilization data helps airport planners optimize gate assignments, identify opportunities for concurrent boarding across adjacent gates, and plan terminal expansion investments based on actual demand patterns.

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H

Heat Map (Crowd)

A crowd heat map is a color-coded visualization showing where people concentrate across an area over a defined time period. High-density areas appear in warm colors (red, orange) and low-density areas in cool colors (blue, green). Heat maps are generated from aggregated tracking data and are used to identify persistent congestion hotspots, underutilized areas, and the effects of layout changes or operational interventions. Unlike real-time density maps that update continuously, heat maps are typically analyzed post-hoc across a time window such as a peak hour or a full day.

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L

Landside

Landside is the publicly accessible portion of an airport before the security checkpoint, including check-in halls, ticketing areas, public arrival halls, curbside drop-off, ground transportation areas, and parking facilities. Passenger flow analytics on the landside focuses on arrival patterns, check-in queue management, and curbside vehicle flow. Landside areas are accessible by the general public, making anonymous monitoring an important requirement for data protection compliance.

Level of Service (LOS)

Level of Service (LOS) is an IATA standard framework for assessing the adequacy of airport space and processing resources for passengers. It grades conditions from Level A (excellent experience, free-flow movement, no delays) through Level F (unacceptable, system breakdown, extreme delays). LOS is calculated primarily from the space available per passenger in processing queues and waiting areas, measured in square meters per person. IATA’s Airport Development Reference Manual defines LOS thresholds for each facility type. Real-time LOS monitoring requires continuous spatial density measurement, which Quanergy Q-INSIGHTS provides using distributed 3D LiDAR sensors.

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O

Occupancy Counting

Occupancy counting measures the number of people present within a defined space at any given time. It differs from flow rate counting, which measures movement through a point. Occupancy data is used for safety compliance (maximum occupancy limits), Level of Service assessment, and retail or concession performance analysis. 3D LiDAR occupancy counting is more accurate than Wi-Fi or Bluetooth-based methods because it directly detects people’s physical presence rather than inferring it from device signals.

Origin-Destination Matrix

An origin-destination (OD) matrix is a data table showing the number of passengers traveling between every pair of origin points and destination points within a defined network or terminal. In airport analytics, an OD matrix might show how many passengers move from check-in to each security lane, from each gate to the central retail concourse, or from different arrival halls to ground transportation. OD data informs terminal layout design, wayfinding signage placement, and staffing allocation at transfer points.

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P

Passenger Flow

Passenger flow refers to the movement of passengers through an airport from entry to departure or from arrival to ground transportation, including all intermediate processing steps. Passenger flow analysis encompasses measurement of throughput at each processing stage, queue formation and dissipation, dwell time in commercial areas, and congestion at bottleneck points. Effective passenger flow management reduces average processing time, improves Level of Service, and increases concession revenue by ensuring passengers have predictable time in retail zones. Quanergy delivers airport passenger flow analytics using anonymous 3D LiDAR and Q-INSIGHTS to provide real-time visibility across the full passenger journey from curbside to gate.

Peak Hour Analysis

Peak hour analysis examines passenger flow, queue length, and crowd density during the highest-demand periods of the operating day or week. Airports design their infrastructure and staffing to handle peak demand without LOS degradation, making accurate peak measurement essential for capacity planning. Peak hour data also informs decisions about when to open additional security lanes, when to schedule cleaning and maintenance, and how to stage boarding sequences to reduce gate crowding. Historical peak hour analysis reveals demand patterns that enable proactive rather than reactive operations management.

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Q

Queue Length

Queue length is the number of passengers waiting in line at a processing point, or alternatively the physical length of the queue in meters. Real-time queue length measurement allows operations teams to monitor checkpoint load continuously and trigger lane additions when queue length exceeds a threshold. Queue length can be measured by 3D LiDAR sensors positioned overhead or to the side of queue lanes, counting and tracking passengers within the queue zone. Quanergy Q-INSIGHTS provides real-time queue length data at security checkpoints and other processing points using anonymous LiDAR sensing.

Queue Wait Time

Queue wait time is the estimated or measured time a passenger will spend waiting in a queue before being processed. Predicted wait time is calculated from the current queue length and the measured processing rate at active lanes. Real-time wait time data can be displayed on passenger information screens, pushed to mobile apps, and used to guide passengers to shorter queues. Accurate wait time prediction requires both real-time queue length data and real-time throughput measurement, both of which 3D LiDAR systems can provide continuously.

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S

Spatial Density

Spatial density describes the concentration of people per unit area at a specific location and time. In airport and crowd management contexts, spatial density is expressed as people per square meter and is the primary metric underlying IATA Level of Service assessment. Spatial density data from 3D LiDAR sensors updates in real time as passengers move, enabling dynamic LOS scoring across the terminal and immediate alerting when any zone approaches unsafe or unacceptable density levels.

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T

Terminal Capacity Planning

Terminal capacity planning is the process of determining how much passenger volume an airport terminal can handle at acceptable Level of Service levels, and planning infrastructure investments to accommodate projected growth. Capacity planning uses historical flow data, peak hour profiles, and processing rate benchmarks to model scenarios and identify where constraints will appear. Real-time analytics systems provide the empirical foundation for capacity planning by supplying accurate, continuous data on how existing facilities are actually being used under current demand conditions.

Throughput

Throughput in airport analytics is the number of passengers processed through a facility, checkpoint, or terminal over a defined time period. It is distinct from flow rate (the instantaneous rate of passage) in that throughput is a cumulative measure. Checkpoint throughput is critical to LOS management: if throughput falls below the passenger arrival rate, queues will grow. Airport planners size security screening, passport control, and boarding gate facilities to achieve the throughput required to handle peak demand without LOS F conditions.

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Z

Zone Analytics

Zone analytics divides a terminal into defined spatial zones and measures occupancy, flow rate, density, and dwell time independently within each zone. Zone-level data allows airport operators to identify which specific areas are experiencing problems, compare performance across zones, and evaluate the impact of changes such as lane reconfigurations or signage updates. In Quanergy Q-INSIGHTS, zones are defined in software overlaid on the sensor coverage map, and zone analytics are available in real time through the operations dashboard.

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