Overview
WEAV is a prototype app that can allow women in urban enivornments safety commute in an affordable manner through the use of womens only electric autonomous carpooling vehicles. The project was wireframed and prototyped in Figma.
Weav
Aim
The design challenge: “Designing Digital Mobility Services & Products for Maximum Inclusion”. We designed an application that would aid women with their daily commute by offering a rideshare app with two main goals: safety and affordability. Affordability is achieved using carpooling with electric autonomous vehicles. This application could expand to cover a larger audience if needed.
Process & Methodologies
We began by initially conducting forensic research. Specific topics we were interested in were emerging technologies such as fourth industrial revolution trends, AI, vehicle autonomy, urban networks, MAAS. For our target demographic we investigated urban mobility for women, which uncovered multiple findings. Women around the world felt vulnerable and marginalised via public network and urban planning around mobility. We decided based on these findings that we wanted to design a solution to improve the safety and wellbeing of womens urban mobility.
For our competitive analysis we conducted case studies of other carpool services, and other services which shared our problem context. We continued to work on three separate application concepts that could potentially aid urban mobility for women. After careful consideration, the team decided that the concept that is now known as WEAV will be idea we continue to support development on. To help flush out the concept we began by creating an engagement matrix, followed by a user journey. This helped us understand the stages of engagement for the user and identify any areas we needed to work on.
We began testing our application using paper wireframes. Multiple sets of paper wireframes were created, and testing yielded results that aided in the final direction for the project. A low fidelity prototype was created in Figma based on the lessons learned and opportunities that arose in the testing. Once happy, we proceeded to cement our work in a high-fidelity prototype in Figma.
