Huddle is an autonomous ridesharing service designed for schoolchildren and their weekly scheduled commutes in dense urban areas.
Context: School of Visual Arts, Master’s Thesis Project
Huddle is a speculative service designed to alleviate the burden of child transit for parents and guardians while improving on the macro issue of low passenger density among passenger vehicles. Huddle uses machine learning to automatically pool young riders in driverless vehicles and transport them to pre-scheduled activities like school and afternoon extracurriculars.
By taking advantage of pooled, autonomous ridesharing in a specific use case, Huddle alleviates parents’ burden of needing to provide afternoon transportation for their children while simultaneously reducing the net number of cars in afternoon traffic.
After interviewing parents in Los Angeles and conducting desktop research, I discovered that low passenger density in vehicles exacerbates the already difficult burden of child transit on parents.
Also, overlapping schedules, lack of transit options, and unique safety concerns make transporting kids a major burden for the over 37,000 US families with school-age kids.
Key Insight: According to the US Department of Energy, the typical car carries only 1.6 people at a time and sits parked over 95% of its lifespan. The key metric for determining value, therefore, is not price, but passenger density.
Key Insight: Arranging transport for kids is a logistical nightmare, and is only made worse when you factor in tight budgets, single-parent homes, multiple children, and work responsibilities.
Synthesis & Framing
After gathering user perspectives and researching the space, I decided to create a service built around solving for passenger density in the specific use case of school and extracurricular transit.
The problem of school-related transit can be largely as a logistical issue, with the central challenge being: how might we most efficiently & safely move schoolchildren to school, activities, and home again?
I envisioned a service that reads a student’s weekly schedule, matches them to other students based on geographic proximity and destination, and pairs them with a driverless ridesharing vehicle. In order to get user feedback on my idea, I created a “sacrificial prototype” and discussed it with a group of high school students.
Key User Insights/Questions
I traveled to Apple Valley, CA to visit a class of high school students and gain feedback on my sacrificial prototype. Their key insights and questions were:
Kids are more vulnerable if their locations are predictable each day.
If a student is sick or is running late, how might the service adjust?
What if one student bullies or harasses another while alone
in a driverless vehicle?
User Acquisition Strategy & Key Performance Indicators
Revised Value Proposition
If I were to continue work on Huddle, I’d like to prototype this service in the real world. At the moment, autonomous ridesharing vehicles are still in their infancy, but I would like to design an analogous study in order to test the idea of pre-scheduled, location-based rideshares for children.