INCLUSIVE PASSENGER EXPERIENCE AUDIT
Who is your terminal actually serving?
the challenge
Understand how Boston Logan Airport's Terminal C actually performs across the full range of passenger types, not just the modal one.
the outcome
Ranked factors affecting the inclusive experience in Terminal C. Detailed wayfinding recommendations, passenger well-being intervention strategies, and a multi-modal data archive (sensor, video, survey, intercept).
TYPE:
Ethnographic Research & Inclusive Design
CLIENT:
Massachusetts Port Authority (Massport) & Boston Logan Airport
KEYWORDS:
Airports, Sensors, Surveys, Ethnography, Mobility, Inclusivity
Similar projects:
Terminal C Passenger Experience Study
Four data streams. Nearly 14,000 passengers. One cross-validated picture.
Standard research produces an average
Averages don't serve anyone particularly well. Passengers with invisible disabilities, first-time travelers, elderly passengers, and non-native language speakers navigate airports differently from the median traveler. Satisfaction surveys and NPS scores flatten those differences into a single number. While that number tells you whether the average passenger is satisfied, it tells you almost nothing about who the terminal is failing, or why
Our Approach
Four data streams. Nearly 14,000 passengers. One cross-validated picture. We deployed a layered data collection approach: environmental sensors tracking air quality, noise, and lighting; overhead cameras processed through a computer vision pipeline; a WiFi-intercept survey reaching nearly 14,000 passengers; and direct intercept interviews at gates. We also conducted guided walkthroughs measuring activity-based and environment-based pain points at key locations across the terminal — capturing what passengers experience, not just what they report.
A finding that appears in one data channel is different from one that appears in four
Pain points confirmed by sensors, behavioral observation, and survey data are structural issues baked into the environment. Pain points that surface in surveys alone are perceptual — real to passengers, but driven by communication or expectation, not spatial conditions. That distinction drives very different design responses. Without cross-channel validation, you can't tell which you're dealing with.
Ranked factors, Targeted recommendations, & A replicable model
Our audit produced a ranked set of factors affecting inclusive experience in Terminal C, detailed wayfinding recommendations tied to specific spatial conditions, and well-being intervention strategies prioritized by impact and implementation effort. The multi-modal data archive — sensor, video, survey, intercept — remains available for ongoing analysis as conditions change.
Our Approach and Expertise