Sway : an adaptive urban mobility platform

Sway is concept for a mobility platform that leans towards user's lifestyle preferences as well as learns their commute patterns in order to make better informed and customized travel suggestions. 

DESIGN RESEARCH  +   ANALYSIS   +   IDEATION   +   PROTOTYPING   +   USER TESTING

Research

We were interested in understanding the logic behind how people optimize their commute options in order to accomplish various tasks. What’s their criteria for choosing a certain mode(s) of transportation over others.    

What we've learned is that commuters are constantly left on their own to optimize their route and figure out what are the best means of travel for them in a given circumstance.

Solution Proposal

Further, from our research, we identified that Speed isn’t the only consideration when choosing what means of transportation to take. Budget, Safety, Comfort, and even Health are factors that play into what is the “best way to get there.” Not only that, but certain circumstances like weather or time of day, may shift your transportation priorities. We envisioned a platform, rooted in machine learning, that would take all of these considerations into account. 

Goal-Setting Features

For individuals who are budget conscious or perhaps fitness/health oriented, the platform would allow for targeted goal-setting as well as a look into their spending or activity history. Persuasive Interaction Design principles and tactics where utilized while developing these features. 

User Testing

In addition to our initial interviews, we've tested the prototype with 10 individuals and video recorded their interactions with the system. We've taken their feedback and are working to further build out the concept.

A paper about the concept has been accepted to be presented at the British HCI (Human Computer Interaction) 2018 Conference. 

TEAM MEMBERS:         Evan Chan         Isabel Dec        PROFESSOR:         Santosh Basapur