SOPHIA SOL KIM
Rideshare Users & Drivers
A user research study of rideshare user and driver experiences in the metro city Atlanta.
Services
Project
Year
User Research, User-Centered Design
3 Weeks
Group Project
2021
Preliminary Research & Interviews
This project's preliminary research follows today's largest organizations of rideshare services, Lyft and Uber.
Research delve into company cultures, financials, hisotry of company, demogaphics, and services offered.
Following preliminary research, our team conducted 2 rounds of interviews regarding the experience of rideshare among 8 participants divded into two categories: Riders and Drivers.
Interview Insights
Riders claim to not value conversation with drivers yet tip higher when they have an impactful connection
Drivers, through situational awareness, have an ability to sense potential safety risks from the rider, car and surroundings
Drivers are marketers and shape how riders experience new places
What do drivers do besides drive?
What we think drivers do:
Pick-up
Drive
Drop-off
What drivers actually do:
Situational Awareness
Communicate
Adapt
Emphasize
Drive
Situational Awareness
Tolerance
Communicate
Drop-off
Adjust car and route to meet rider preferences
Adjust quickly to a safe pick-up location
Read emotional needs of riders and respond accordingly
Greet Riders to make them feel comfortable
Sense if rider is in danger or neighrborhood is unsafe
Recommend local spots. Talk as much/little as riders want
Drop-off at a safe location
Being patient with impatient and testing riders
Are drivers replacable?
Riders Value Most
Drive
Situational Awareness
Empathize
Adapt
Easily Automated
Difficult to Automate
Tolerance
Communication
Riders Value Least
Why do we need rideshare?
Explore our context to category categories.
Context to Qualifier - Riders
Common
​
Context
​
Activity
​
Qualifier
Need for Independence
​
Need to Locomote
​
Need to access public transportation​
​
Need to take Uber/Lyft
Design Imperatives
Design Solution
Our team proposes the case to have a human presence present in rideshare vehicles in an autonomous future for the preservation of human softskills. A prevalent human presence could be further developed and expanded to an algorithm that matches user-to-chaperone & user-to-user based on similar interests, language, and more.