Human-Computer Interaction: Waiting

Project Status: Completed

In my Human Computer-Interaction course, taught by Lauren Wilcox, our semester-long group project centers around personal informatics. As busy students, our group decided to explore waiting times and the ways to optimize time management. In this project, I served as project manager, writer, and assisted with research and design.

For the project, our research focused on undergraduate students who live on campus that might have class back-to-back and only have a limited time frame to get lunch. In general, it is really inconvenient to arrive somewhere and have to wait in really long lines. Currently, students have to gamble where the lines might be shortest. They also have to factor in walking distance or taking transportation. Are the lines longer at the restaurants in a different area of campus? Where should they go? Is it faster to walk or take campus transportation?

Quick Links:

Early Project Report

Affinity Diagrams

Ideation Documentation

Usability Report

Interactive PDF Presentation

Final Design Document



Initially, we decided to explore the waiting patterns of the Georgia Tech community in order to discover some of the pain and gain points associated with both waiting in line for food and waiting in line for transportation. To us, they connect because in a limited lunch period, you need to decide where to go to eat based on how long it will take to walk as opposed to transportation and also how long the lines might be at that restaurant.

Through this research process, we refined our idea and scope to focus just on undergraduates, rather than the whole Georgia Tech community, because they’re the ones who live on campus and have meal plans. We refined it further to focus solely on waiting for food, but we would not have reached that conclusion without going through the research process first.

Task Environment

To more fully think through the process someone goes through when getting food during lunch, we decided to focus on an example of a student going to the student center. We created a very high level task analysis and a more in-depth storyboard. After that, we did a competitive analysis of the related technologies that attempt to solve similar problems.

Hierarchical Task Analysis

At a very high level, the typical student will decide to get food because she has just enough time. She walks over to the student center and chooses Panda Express because it is her favorite. Except today, the line is too long, so she walks over to Subway which has a shorter line, but still a line. Then she goes to class.

This overview misses some components, but it gives the general overview of the key actions taken by a student who wants to go buy lunch.


Based on that general overview, we thought of a specific story of a student named Danny. Danny gets out of class and has just about an hour to get food. He has to factor in that it will take him 20 minutes to get to his class from the student center. He never takes transportation because he doesn’t have the time to wait for a bus or trolley. He’d rather not chance it and walk.

Danny is frustrated that the lines in the student center are never consistent. Sometimes the line at Panda Express looks really long, but really it goes fast. Today, he gets in the Panda Express line and it doesn’t move. Fifteen minutes later, he only has 45 minutes to get food and go to class. He can see that the Subway line has nobody there, so he goes to Subway and waits for them to make his food. Seeing that he has twenty minutes left, he takes his to-go sandwich and races over to class. He made it to class on time, but he’s hungry and his hot sandwich is now cold. He has to try to eat discreetly in class, while also trying to pay attention and take notes.

Competitive Analysis


In order to understand the problem space of transportation-related applications, we looked at the MARTA On the Go app, the My Transit -NYC app, the GT Buses app, and GT NextBus. MARTA On the Go and My Transit-NYC function to help city transportation users plan their travels. GT Buses and GT NextBus are both Georgia Tech specific.

Feature MARTA On The Go My Transit – NYC GT Buses GT NextBus
Next Arrivals X X X X
Daily schedules X X
Static Route Map X X X
Alerts X X X
Notifications X
Favorites X X X
Trip Planner X* X
Built-in Navigation X
Near Me X* X X
Breeze/transit card management X*

The best system is one that is personalizable and autonomous. Once the user sets up alerts/notifications based on their personal commute, they will automatically be kept up to date and reduce the need to actually open the app.

Important functionality and information should be displayed immediately when the app loads. For instance, My Transit displays delays/alerts on the Home screen which has more immediate value to the user than a “what information would you like to know?” screen like on MARTA. Alternatively, for the MARTA app, showing ‘Favorites’ on load is better because it has a higher chance of being the information the user wants. Also, MARTA has multiple pieces of functionality in the top-right dropdown menu of each screen. There seems to be no reason for this placement besides the fact that they ran out of room on the bottom-nav. There should be some kind of hint/tip that informs the user of this functionality.


Apps should be a one-stop-shop for all things transit. Ideally, a transit app should be customizable to the user’s commute and also allow the user to manage their transit cards (i.e. checking balance, adding trips). MARTA  has a Breeze Card section but it is read-only and doesn’t work properly. Also, the “trip planner” redirects the user to Google Maps. Reducing the amount of apps needed in a single experience would make the whole process more efficient.


To understand the related solutions for dining, we looked at Tapingo, Grubhub, and GET Mobile. Tapingo and GET Mobile are specific to college campuses while Grubhub provides a good general model for food ordering.

Feature Tapingo GrubHub GET Mobile
Pickup vs. Delivery X X
Nearby X X X
Approx. wait time X X
History X X X
“Your Usuals” X X X
Hours X X X
Location w/ static map X X X
Ratings X
Price Range X
Reviews X
Account Management X

User-centric information includes information oriented towards the user like wait times, near me, history, etc. This functionality will be used most frequently especially if the user already has restraunt preferences.

Restaurant info contains specific restaurant information that provides supplementary data to help users make a decision (i.e price range, reviews, ratings).

As students are generally on a limited meal plan or budget, account management tools can be useful and also provide data that might sway a user’s decision (i.e. balance, add funds, transaction history). This touches on the “one-stop-shop” idea from the Transit section; to provide convenience and increase efficiency.


Account Management, in the form of directly managing funds, was a feature missing from Tapingo and Grubhub. Tapingo allows you to link a student account and use plans to pay for food, however, there is no functionality to actually add funds in the app.


After examining these apps, we were able to extract both general and specific features that we can integrate into our project. In general, apps that go beyond simple reporting and use their data to provide higher order functionality like trip planning are more appealing to the end user. MARTA On the Go forces the user to use multiple apps for trip planning (MARTA + Google Maps) while My Transit integrates it right into their app. On the food side, Grubhub provides restaurant information like ratings/reviews eliminating the need to go to another app like Yelp.

Another common feature in some of the apps we analyzed was account management. Anyone who has taken public transportation or used student funds knows the inconvenience of reloading cards or checking balances. We have the opportunity to leverage buzz fund and dining point management functionality like Tapingo. The existing apps all include strong features, but lack depth that can turn them into robust experiences.

Description of Context

Looking beyond campus and those associated waiting times, a system that can address waiting times and maximize efficiency can be applied to a variety of situations. It’s not just students who have a shorter lunch break. Pretty much anyone in the working world has a lunch break and might want to grab something really quickly. But both transportation and dining can have unexpected setbacks. There could be a power outage that causes the transportation to stop working. There could be a big group that comes into a restaurant. Many factors.

The technologies could include an app for notifying the users, sensors within the stores and transit, and utilizing the transportation APIs. This system could go several ways. When we first talked about it, we called our idea “Waze for Dining” or “Waze for Waiting Times.” Waze, the map tool, uses live user input to update drivers about any traffic hindrances. Something similar could be done with the meal waiting times. However, the problem is always with unreliable users. There have been examples of people just spamming Waze.

How can we provide users with a simple, efficient way for them to maximize their time?

Research Report

Research Method: Intercept Interviews

The best way to obtain the most accurate information is to talk to the target demographic. We decided to conduct informal, intercept interviews with students who were waiting in line for food and waiting in line for transportation. We wanted to capture user needs while being able to dynamically pose questions based around the conversation. We had a short list of questions to start the conversation, but everything else was freeform. We decided that performing a structured interview would not work in the natural setting when students could leave at any time to get on their buses or get their food. An added bonus of interviewing students in their natural setting was that concerns about waiting were likely already on their mind.

Research Method: Surveys

Since we could only reach a limited amount of people and we wanted to gather hard quantitative data, we created a survey on Google Forms that we could send out.  We were interested in data to see what services students currently use, how often, and what they like and dislike about them. We selected the Georgia Tech Reddit community, better known as “r/gatech,” to post the survey. “r/gatech” has about 10,000 subscribers so we had access to a wide pool of potential survey-takers, and Reddit is a commonly used social media platform by college age students. We do recognize that the user base for Reddit is mostly male, however. That being said, 88 responses were recorded and 12 responses provided their email address for followup questions. We asked the 12 respondents specific questions about their routine and their thoughts on suggestions for improvements. Of these 12, 6 responded.

Target User Group Number Observed Number Interviewed Notes
Students who utilize GT transportation 0 10 The number reflects the people we talked to in person and who responded to our follow-up questions from the survey.
Students who utilize GT dining services 0 4 The number reflects the people we talked to in person and who responded to our follow-up questions from the survey.
Employees of GT transportation 0 1 A trolley driver started talking to Larry while he was interviewing other students and gave insightful feedback from the employee perspective.

In the survey, we included a section for students to provide their email address if they were willing for us to follow-up with them. We had twelve students willing to do so and we gleaned additional insights from those discussions regarding what they would do to improve transportation and dining services.

This process of reading the responses from the post on “r/gatech” informed our understanding of the problem. We realized that while we connect food and transportation, a lot of people actually do not. Thus, in scaling our project, we decided to focus on waiting for food.

Analysis of User Research Data

As a team, we reviewed all data (individual participant quotes, closed-format survey responses, open-format survey follow-up responses) and used these to create individual affinity notes, each representing one idea, in order to view trends across the research findings. We then created an affinity diagram where we grouped related ideas. We believe that these research methods enabled us to get both the qualitative and quantitative data we need to design solutions for our problem space. This activity led to getting a group consensus about what primary issues should be addressed in our design and whether or not the scope of our project should be narrowed.

Method Data review and analysis approach Rationale for approach
Interviews with ten members of the target user group In a bottom-up fashion, we constructed an affinity diagram as a group, with the affinity notes based on findings from our intercept interviews. (See Appendix E for affinity notes.) The analysis yielded five pink notes: “based on my time, I can do X,” “environment is a constraint,” “technology supports time management,” “speed is important,” and “features and services.” The affinity diagram

allowed us to more easily see

trends in issues across the people we interviewed. While we did have a feeling for what the issues might be beforehand, having to organize everything forced us to consider common themes among the data.

Survey with closed-format questions to understand student food and transportation preferences
Closed-format questions asked:

  • Where they have lunch
  • How long are they willing to wait in line for food
  • Why they eat where they eat
  • Tapingo app usage
  • Tapingo reliability with a Likert scale (1 = extremely negative, 5 = extremely positive)
  • Campus transportation usage
  • How long would they wait for a bus
  • Nextbus app usage
  • Nextbus reliability with a Likert scale (1 = extremely negative, 5 = extremely positive)

Open-format question asked

  • Email for follow-up
We couldn’t use the data for the affinity notes, but we were able to analyze the survey for trends in behavior and preferences.
Data about reliability were treated as interval data.
All other data from all closed-format questions were treated as nominal data.
The closed-format survey gave us hard, quantitative data to analyze larger trends and preferences for students. For instance, we learned more about the most popular locations students choose for food and transportation. (See Appendix F for a screenshot of the survey results.)
Data about reliability was treated as interval data since we wanted to calculate values like mean and median to see which app is better received by students.
All other data was treated as nominal data since we wanted to visualize that data in bar charts and pie charts.
Follow-up survey with open-format questions to understand student opinions of transportation and dining. (See Appendix C for follow-up survey.)
Open-format questions captured free-response data about how they traveled to get food, what could be improved with transportation and dining, and opinions about the Nextbus and Tapingo apps.
Open-format questions containing free response data were used to create affinity notes. Note that some responses came after the day we made the affinity diagram, so they are not present in the affinity notes. Nevertheless, we had a meeting to discuss the new responses. We realized too late our survey had several blind spots. It had no questions about demographics and was multiple choice only. While it was too late to remedy the former because only 12 people were open to a follow-up, the latter was fixed by including open-format questions.

According to the survey, the most common locations for eating lunch are the Student Center at 75% and the food court in the Student Center at 44.3%. Note that users could select more than one answer for this question. The vast majority of students need at least 30 minutes between classes to feel comfortable going to get food. 58% of students choose where they eat based on their food preference, while 28.4% go to the shortest line. 75% of students use campus transportation, and, of these 75%, over 63.3% wait at most 10 minutes for the Trolley before leaving. Tapingo has an average rating of 4.2, which is higher than the 3.6 rating of Nextbus.

From our followup survey, Tapingo is better than Nextbus since Tapingo is very convenient, while Nextbus is unreliable since the predicted times do not reflect the actual times. Of the six responses, only one user sometimes rode the Trolley to get food, and the five others either walked, rode bikes, or skateboarded. Time optimization is a key consideration for students because all responses showed students want to travel across campus as fast as possible.

The affinity diagram generated five pink notes, which can be separated into two groups. The first group concerns the balance between “based on my time, I can do X” and “speed is important.” In other words, students want to perform actions that are not 100% time efficient like being environmentally conscious but still must balance them against time. The second group revolves around students dealing with the constraints of the environment with technology. However, technology has limits, as expressed in the features students wish they had access to.

Prototype Evaluation

I conducted usability tests with potential users of the project as well as peer testing. View the usability report.

I designed this poster