We Commuters

We Commuters is a mobility app developed during the pandemic to provide safer and more flexible commuting options. It integrates public transport, car-sharing, and ride-sharing while prioritizing real-time updates, accessibility, and sustainability.

About

Year

2021

Timeline

4 weeks

Tools

Figma, Miro, Adobe Illustrator

Process

Discover, Define, Ideate, Design, Test

Methods

User Interviews, Affinity Map, User Persona, Empathy Map,

My Role

UX Research, UX/UI Design, User Testing

Challenge

The Covid-19 pandemic has changed the mobility behavior of many people. Commuters had to deal with reduced public transport capacities, hygiene concerns, and inefficient options for carpooling. This led to an overburdened infrastructure, an increase in individual traffic, and higher CO₂ emissions.

The Covid-19 pandemic has changed the mobility behavior of many people. Commuters had to deal with reduced public transport capacities, hygiene concerns, and inefficient options for carpooling. This led to an overburdened infrastructure, an increase in individual traffic, and higher CO₂ emissions.

Solution

WeCommuters is a digital platform that connects commuters. The app allows users to easily organize carpools and promotes sustainable mobility through an integrated reward system. With its simple usability and attractive incentives, more people are encouraged to use the app, leading to a growing community and increased efficiency of carpools.

WeCommuters is a digital platform that connects commuters. The app allows users to easily organize carpools and promotes sustainable mobility through an integrated reward system. With its simple usability and attractive incentives, more people are encouraged to use the app, leading to a growing community and increased efficiency of carpools.

Dicover Phase

To gain a comprehensive understanding of commuter mobility, we employed various user research methods. These included qualitative interviews with users and industry professionals, Affinity Diagram analysis to identify patterns, "How Might We?" questions to translate findings into opportunities, and persona development to represent key user groups. These methods allowed us to create a solution tailored to real user needs.

Qualitative Research

To gain deeper insights into the challenges and opportunities in commuter mobility, we conducted interviews with twelve participants, divided into two main groups:

To gain deeper insights into the challenges and opportunities in commuter mobility, we conducted interviews with twelve participants, divided into two main groups:

Service Providers

This group consisted of professionals such as subway and suburban train drivers, Uber drivers, a spokesperson from Voi, and employees of StadtTeilAuto Freising. Their perspectives provided valuable insights into operational challenges and service constraints within the mobility sector.

Users

This group included public transport users, ride-sharing users (such as Uber and taxi riders), and car-sharing service users. Their insights helped us understand the commuter experience from a user perspective, focusing on their needs, frustrations, and expectations.

Key Questions

For Industry Professionals

  • What are the biggest challenges in providing efficient mobility services

  • How has the pandemic impacted passenger behavior and service demand?

  • What solutions could improve commuter experience and service efficiency?

  • How can mobility providers encourage more sustainable commuting habits?

For Public Transport Users

  • What are the main challenges you face when commuting daily?

  • What factors influence your choice of transportation?

  • How do you perceive existing mobility solutions, and what would improve your experience?

  • What motivates you to choose sustainable transport options?

Affinity Map

The collected interview data was systematically structured using the Affinity Diagram method. We identified recurring patterns and grouped key insights into overarching themes. This helped us to define the main challenges and derive targeted solutions. We conducted interviews, not only with mobility companies but also with users of public transport and other ride-sharing services such as Uber. This allowed us to consider different perspectives and gain a more comprehensive understanding of mobility challenges and the needs of various user groups.

Key Insights & Possible Soltions

Key Insights

Possible Solutions

Public transport inefficiencies drive users toward individual transport.

Provide real-time public transport updates and integrate ride-sharing options to improve reliability and offer flexible alternatives.

Carpooling adoption is limited by trust and accessibility issues.

Implement a user verification system with identity checks and a rating system to enhance safety and reliability in carpooling.

Gamification and incentives encourage sustainable mobility.

Introduce a reward system where users earn points for shared rides, which can be redeemed for discounts or benefits.

Improved communication and verification features build trust.

Develop an integrated chat and group feature to help users coordinate carpools easily and securely.

Key Findings

The interviews revealed that around 70% of suburban train users are commuters, making them a primary target group. During the pandemic, many people avoided public transport, leading to an increase in individual traffic. At the same time, car-sharing services were underutilized as organizing carpools was perceived as complicated. Sustainable mobility could be made more attractive through incentives.

Define Phase

To gain a comprehensive understanding of commuter mobility, we employed various user research methods. These included qualitative interviews with users and industry professionals, Affinity Diagram analysis to identify patterns, "How Might We?" questions to translate findings into opportunities, and persona development to represent key user groups. These methods allowed us to create a solution tailored to real user needs.

User Persona

Based on the collected data, we created user personas representing typical commuters. These helped us understand user behavior, needs, and frustrations, allowing us to tailor design decisions accordingly.

Based on the collected data, we created user personas representing typical commuters. These helped us understand user behavior, needs, and frustrations, allowing us to tailor design decisions accordingly.

Empathy Map

Based on the collected data, we created user personas representing typical commuters. These helped us understand user behavior, needs, and frustrations, allowing us to tailor design decisions accordingly.

Based on the collected data, we created user personas representing typical commuters. These helped us understand user behavior, needs, and frustrations, allowing us to tailor design decisions accordingly.