CTA Companion
Overview
What is CTA Companion?
A 13-week application project made for employees and commuters that promotes a clean CTA environment. This is through conversations between the employees and bots that will clean the buses, and trains. There will also be a version for commuters to help with staying safe. It was made to address the challenges of smells, and safety issues that ruin the common commuter experience.
Team
Individual
Tools
Figma, Erika Hall's Principles of Conversational Design
Problem
The CTA commuter experience is being gradually compromised by the presence of unpleasant odors, specifically related to feces and defecation. Numerous grievances about the presence of marijuana smoke are also prevalent. Most of the users of CTA feel unsafe too.
I started out by first doing online research on the CTA Commuter Experience. I found a lot of information from the WBEZ Chicago CTA surveys which had 2,000 responses. After, I began to interview others about their experience with CTA. There were some very useful insights in this research.
In my personal survey research:
More than 70%
were concerned with safety.
“I take the Red Line almost every day for work and there’s always this anxious feeling like, ‘What’s going to happen today?’ I’ve seen people being harassed, assaulted, I’ve seen people doing drugs and defecating on the train. It seems to have only gotten worse since the pandemic.” - Sean Macleish (WBEZ Chicago 2023)
When asked to rate the smell of CTA transportation vehicles
Only 20% rated it 6 or higher.
Information Architecture
Utilizing information architecture, I've compiled a list of valuable features for the application.
There are currently two separate applications designed for commuters and one tailored for employees. Based on the feedback received from the personal surveys, employees received significant praise, even though they were unhappy with CTA. Armed with this insight, it's clear that enhancing the employee experience through the application has the potential to boost their performance, consequently benefiting commuters as well.
From the surveys, the features that came about were:
(Employee)
Cleaning buses/trains: The process involves communicating with the interface to get the bots to clean the buses or trains. Helps with the smell of the buses and trains.
Finding Clean buses/trains: Communication with the interface to find buses cleaned by bots already. You are then linked with the location.
Reporting Crimes: If you witness crimes or you feel you are in danger you can use the application to report. Includes accurate times of how far help is.
Time-Efficient Clock In/Out with Badge System: Quick one-second clock-in and clock-out to help workers focus on other things.
(Commuter)
Call for help/Report Crimes: Made to help commuters who feel unsafe, or commuters who are witnessing crimes. Help each other stay safe.
Navigating Chicago: Put in where you are trying to go and get the buses and trains needed along with the "How Long?" features.
How long?: Find out exactly how far buses and trains are with accurate tracking on maps. Made to prevent ghost buses and trains.
Wireframes
Low-Fidelity
I began the project by sketching out my ideas on paper, aiming to create low-fidelity wireframes to visualize the user experience flow. Within these wireframes, I introduced several key features.
One significant feature I incorporated is the "how long" feature, designed to provide live updates on bus locations. This feature is particularly valuable for CTA customers as it helps prevent situations where buses seem to vanish ("ghost buses"). It leverages real-time tracking of buses, delivering the live feed directly to users.
Another essential feature I developed is the "find a bus" function, specifically intended for employees, particularly the bus drivers. This feature facilitates communication between the drivers and the companion bots responsible for cleaning buses. By connecting these components, we address the issue of odors on both buses and trains, significantly improving the overall commuter experience.
Mid-Fidelity
After taking a look at the low-fidelity prototypes I decided to start on mid-fidelity. I wanted to get these tested prototypes tested. I included the find a bus feature where I have automated responses to the bots to provide efficiency and quick conversation. Along with truthful bots this brings the entire experience together.
Hi-Fidelity
Takeaways
Prototypes
Something I didn't like that I did in this project was working with new interfaces. I am very used to Adobe XD, but I decided to work with Figma for the first time while doing this project, and that's why I think my prototypes aren't as good as they could be and they didn't come out the way I envisioned them in my original low-fidelity prototypes. If I could do this again I would work with Adobe XD while I develop my figma skills on little projects. However, I am sure that with some feedback I could make this look a lot better with Figma.
Learning that I don't have to end projects with mockups
This came to my head after doing the mockups. but maybe this project was better as a conversational interface project. After getting interviews and feedback from CTA users and drivers I figured that my idea only solved some of the problems, but I think trying to solve everything I didn't complete it the way I wanted to. This idea was originally meant to solve the complaints of dirtiness and smells that users have. It was brought up because it was something I myself went through and I figured talking about that would be useful, and in the project, I discussed deeply how the interface would work. How the bot would converse with the users of the application. I discussed things like manners, truthfulness, personality, and relation in conversation. All things that I feel like I somewhat missed in the mockups.
Thank You!