SUMMARY
MHCI Captsone Project -Crafted future-ready airport designs for ACAA, enhancing passenger flow and queuing efficiency at Pittsburgh International Airport.
TEAM
Me (Interaction designer)
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Emily Yang (PM)
Ketaki Rao (Developer)
Lauren Jablonski (Researcher)
Irene Yu (Researcher/Designer)
Denny Check (Visual Designer)
TIMELINE
January 2020 -
Augues 2020
Reimagining the Waiting Experience for the Era of No-contact Air Travel
Virtual Queue Service for the Future of Air Travel 2025
In collaboration with Pittsburgh International Airport's Terminal Modification Program 2025, our MHCI capstone project at Carnegie Mellon University aimed to shape the future of air travel. Our designs, poised to influence the airport's UX for 2025, led to the creation of two minimum viable products, complemented by a detailed service blueprint.
As the tech lead, my focus was on interaction design, tackling the intricate challenge of airport crowd management and virtual queuing systems. Through a series of roadmaps and service blueprints, I crafted a framework to manage the ebb and flow of passengers efficiently.
Challenge
After the 2019 pandemic, we moved the project into remote mode and strategized how to make the design work within the new situation. To test our assumptions, we used usertesting.com to mimic the airport check-in experience and simulate the check-in experience using figma.
Outcome
We presented our solutions to the executives and head of the airport experience on July 31st, 2020 in a zoom session. Along with that, we delivered a client report, service blueprint, working prototype and a detailed user research playbook.
Overview
TL;DR
Our research-driven approach aims to inform the public sector to reimagine the passenger experience.—making journeys less about waiting and more about enhancing passenger flow and queuing efficiency.
“A user’s journey doesn’t begin at the airport—it actually starts at home...
it was about hitting those emotional notes that turn a trip into a journey."
Research Phase I
Discovery
From waiting psychology to crowd behaviors
We engage with the users early and ask the obvious questions:
"Why and how do queue lines form?"
"What dominates the emotions of the waiting crowds?
"How do people react to unexpected buffers? "
Next, over the course of 3 months, we conducted 15+ research activities with over 100 users to better understand the psychology and behaviors associated with waiting. We conducted field research, interviews and explored analogous worlds including Disney World, Wholefoods and etc... We took a deep dive into The Psychology of Waiting by Don Norman (2018).
Examples of our research activities:
From in-depth interviews, diary studies, and airport travelers shadowing we found out that people often associate lines with negative experiences at the airport due to the Lack of Clarity, Perceived Unfairness, and Unoccupied Time.
We identified four types of traveler personas:
Anxious Travelers, Perfect planner, Frequent Travelers, and the Explorer.
Research Phase II
Ideation
"How can we eliminate the anxiety?"
We formulated the main hypotheses based on what we observed from the discovery phases,
At this stage, we synthesized our findings and feedback with an affinity diagram. We focused on issues that appear to have a more extensive cluster on our diagram.
We then generated storyboards and tested them with speed dating. The user's rating informed us of the acceptance factor of each idea and filtered out questionable ones.
We created storyboards to reflect the below notion:
1. For average travelers "Valued Time" and "Perceived Fairness" are factors that could play into how willing they are to trust the system.
2. "End Strong" ! The last touchpoint will define the overall impression of the journey
Define
Strategize, Prioritize and Visualize
We consolidate our user findings into concrete and succinct user statements with How Might We (HMW). The formulated design proposals were put into the prioritize matrix and stakeholder map to clear our heads a bit on things we are trying to solve.
Using the prioritization matrix we identified problems that impact directly travelers' experience during recall sessions. With the highest one being "controlling how many travelers show up in one single time window".
We sought to build out a comprehensive virtual queue system that can curb the queueing overflow and assign walk-in time windows based on live traffic predictions.
The decisions were documented with a service blueprint as a final source of truth for the project. It helped us map out promising ideas that provide control and efficiency to travelers. Via Service Blueprint we found out that:
Provide a certain level of consistency from curbside to airside to attract higher service retention; otherwise, there will be no trust in the system.
Prototype I
Line Simulation Study
Due to the Covid 19 Situation, we had no access to the airport, thus we recreated a simulated airport security check line experience to study the behavior people have while waiting in line or being offered alternatives such as a pass to skip the wait.
"How can we give value to people's wait?"
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We asked 30+ participants to take part in a queuing simulation.
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We let participants choose between a buzzer and a physical queue.
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We wanted to see how people reason the trade-offs with time and expectation in line.
Prototyping the experience:
Interacting with "Arch"
Prototype II
User Interface and Logic
Conversational Flow weaves out failed logic quickly
We mimic an actual chat service with dummy flight info and used this to waterproof our logic steps.
1
2
Wizard-of-oz (WOZ) We created the prototype in Figma and performed predictive trend forecasting via wizard-of-oz data analysis (we manually calculated the itinerary recommendations based on PIT historical data, but told the participants that the system had done so algorithmically). Users received a Google form with specific questions several days before departure that asked for their preferences such as cushion time at the airport, whether they would be checking a bag, etc. We collected and analyzed the user's actual journey experience.
"What do people expect from a smart VQ service?"
From this WOZ stage, we verified challenges such as:
- A diverse tech-literacy level means not everyone is willing to delegate their task to our proposed service.
- people with constantly changing agendas expect to have chaos but are more open to trying out new services.
Thus, we went in the end for dual-channel solutions - a chat-based system with a lower barrier for one-time visitors AND a full-fledged app for frequent travelers.
MVP delivered realistic interactions with Facebook API
We built a functional Chatbot with Rich Text Messenger features.
"How can we build trust with conversation?"
Conversation Flow of First Time User (FTU) Flight Inquiry
Evaluation
Remote User Testing
We moved to remote working starting March 2020 and tested our mock-ups and assumptions with in-depth sessions at Usertesting.com. Our users were asked to lead us in cognitive walk-throughs.
Solution
By pivoting our solution, we are now focused on how travelers can easily choose a queue strategy, accept the time window provided by VQ, and proceed to the gate without stressful situations.
I was the tech lead on the team, focusing on interaction design. To model the mass complexity of airport crowd-management and virtual queue services, I created multiple road maps and service design blueprints.
Design
Plan Your Flight
Get insights into every step in your airport journey.
Enroll in Virtual Queue
Reduce congestions and wait times in airport security check-points.
Enter your flight number and get personalized suggestions with option to join the Virtual Queue
Work-in-progress screens:
Virtual Queue for the Era of No-contact Air Travel
Lastly, trialability.
Simply, how easy is it to try out the system? For Quickline, we suggest promoting trialability by saving 20% of time windows for walk-up only, only travelers until the adoption rate grows.
"VQ is kind of like putting your name on a waitlist at a restaurant and then they call you in when it's your turn "
Together, We Stay Strong
