Transit.
Personalizing public transportation through LLM-based AI assistance.
Details
Challenge
Brainstorming apps that are used at least once a week and has potential to incorporate AUI to facilitate the user flow.
Duration
Fall 2023 | 1 week
Tools
Figma, Research, Matchmaking
Getting Started
About Transit
Transit app is a mobile application that provides real-time information about public transportation options in cities around the world. The app allows users to easily plan their route using various modes of transportation, including buses, trains, subways, and bikeshares.
The Concept
How does the implemented AUI work?
Transit currently
The current Transit UI requires 5 steps for configuring a destination route regardless of where the user is headed.
Paintpoints
Redundant user inputs from app launch to navigation.
Transit does not recognize repetition in patterns.
User is likely to be late by missing the best option.
When user is in a rush, friction points are amplified.
Proposed AUI
The proposed AUI streamlines the process into 3 interactions, reducing friction from 5 steps. It saves time by simplifying route comparison. Users are alerted to the best route. Early notifications help users avoid missing their commute and being late.
What it aims to do
Notify the user before estimated arrival.
Prediction of potential destination and/or route.
Repair of inference errors when the prediction fails.
Suggestion of fastest transportation method.
01
Notification based interactions
Transit will notify users based on travel patterns.
The AUI system will alert users based on their travel patterns, allowing them to check bus arrival times without unlocking their phones or accessing the Transit app.
02
Search / spotlight based interactions
On spotlight, Transit will recommend destinations.
Implementing a "easy Transit" option to allow users quickly add bus information based on AUI detection of past destinations.
But what if it gets my predicted destination wrong?
The AUI system will also offer anticipated destinations based on time and location, along with additional bus line details for each recommended destination.
03
In-app options to add specific bus information
A streamlined approach for accessing precise bus information without constant app navigation.
04
Implementation of visual queues
Users can promptly estimate the distance of a bus visually.
The visualization will adjust colors based on the bus line.
Profitability
Value Creation (user)
Reduces friction in customer experience.
Increases user reliance on app for accuracy.
Saves valuable time for the app's users.
User is less likely to miss public transportation.
Revenue Creation (transit, partners)
Increased user reliance on transit, leading to higher MAU and app usage frequency.
Improves user retention and subscription revenues for transit.
Increases in-app payments / revenues for partner agencies.
Potential to increase premium pricing in the long term with minimal impact on user volumes.
Return on investment
User Acceptance
Reduces friction from 5 steps to 3.
Saves time spent comparing routes.
Alerts user to the best route to destination.
Helps user avoid missing their commute with an early notification.
Financial Viability
Increases app retention and repeat payments for transport agencies and Transit.
Technical Viability
User travel history logs need to be stored, processed, and used to train the algorithm in order to provide adaptive predictions for users.
Sources of data include open APIs from transport agencies and real-time crowdsourced data.
Heysu Oh
Last Updated : Nov 2024