How might the San Francisco Municipal Transportation Agency (SFMTA) improve the customer information experience?



Help the SFMTA conduct  foundational user research to inform and influence Muni's Next Generation Customer Information System product strategy


A design strategy and recommendations used to  inform the initial Request for Proposal (RFP),  as  well as  an expanded internal human-centered research and design capability



Evolution of transit choices since 2000 when the SFMTA implemented Nextbus

Evolution of transit choices since 2000 when the SFMTA implemented Nextbus

More than 15 years ago, the San Francisco Municipal Transportation Agency (SFMTA) became a national pioneer when it implemented its first-generation real-time customer information system on its Muni transit network.  Times have changed, however.  People have grown accustomed to an ever changing technological landscape that continues to redefine the transportation industry through on-demand services and real-time information throughout the transit journey. Over the past 15 years, San Francisco has seen an explosion of car sharing, bike sharing, private employer shuttles and mobile app-based private commuter vans. With these new services, public expectations have risen, making it more challenging to meet customer needs with the current information communication model. As transit agencies seek to increase ridership, these changes are prompting transit organizations not only to adapt but to chart out an innovative vision for the future.


As a San Francisco Mayor's Office of Civic Innovation Fellow and User Experience Researcher, I worked with the SFMTA to lead foundational user research for the the Agency's Next Generation Transit Customer Information System (AKA the Nextbus Replacement Project). Our goal was to uncover insights that will drive the the SFMTA's strategy to adapt and innovate in today's rapidly-changing and competitive transit landscape. While working to share my knowledge about human-centered design, I aimed to arrive not as an "expert" but rather a collaborator whose primary goal was to learn and wherever possible, contribute user research and design best practices to support the SFMTA's vision of transit in a digital age. 



What is the Next Generation Customer Information System? 

We all know the frustration that grows inside when you realize that the bus for which you've dutifully waited is not coming according to the Nextbus prediction. Even worse, we all intimately know the overwhelming uncertainty that quietly gnaws at you, as you sit stationary on the N-Judah train that is halted in the darkness of the underground tunnel. What's going on? Why is the prediction wrong? What is my bus late? 

The SFMTA is working to meaningfully address these important customer challenges by providing more accurate and contextual real-time information through digital on-board signage, updated shelter signage, and an improved mobile platform. With the Next Generation Customer Information System, the SFMTA aims to increase public confidence in Muni and improve the commute experience. 

CIS Diagram.PNG
The SFMTA is planning a new system that puts more information into the hands of our riders to help them make better travel decisions. We understand how vital this information is and are looking to blend advancing technology with the current and future needs of riders across the region
— Director of Transportation, Ed Reiskin



Research was conducted in diverse neighborhoods throughout San Francisco. Focus groups and 1:1 interviews lasted 90-minutes. Discussions were focused on how different customers characterize, locate, and use information. Our key questions were:

  • How does the presence of information impact mode choice?
  • What are the contextual factors, reasoning, and motivations behind customer mode choice and information needs?


We wanted to speak with Muni riders who demonstrated a wide range of commuting behaviors such as:

  • Regular and Non-Regular riders
  • Riders who connect to Muni from other transit systems
  • Riders who transfer regularly 
  • Riders who commute very late at night or in the early morning
Planning 3.PNG


  • In-Depth Interviews
  • Focus Groups
  • In-Context Immersive Ride-Alongs


  • Concept Testing
  • Competitive Analysis
  • Surveys


To better understand current Muni customer attitudes and behavior, we conducted one-on-one interviews and focus groups with key stakeholders including the Senior and Disability Action Network, Independent Living Resource Center,  LightHouse for the Blind and Visually Impaired, and the Youth Commission. In addition, we went out into the field to hold immersive “ride-along” research sessions with customers where we shadowed them as they commuted.

We wanted to understand and creatively capture customers' attitudes towards Muni services so we went out into the field and asked

Focus group with the Independent Living Resource Center of San Francisco 

We spoke with youth riders and asked them to write a love / breakup letter to Muni

Mental Model of the Muni commute experience in San Francisco

Competitive Analysis of existing transit mobile applications

Interview data was analyzed and coded using RQDA within R to identify key themes



We wanted to measure how real-time information delivered at different points throughout the customer journey could influence mode choice and help the SFMTA retain customers who might otherwise use another, less sustainable transportation mode during a delay. We had three key questions:

  • Can providing nearby alternative routes with shorter waits help retain transit customers who might otherwise use another transportation mode? 
  • How do customers respond to transit information disseminated through third-party 20 mobile apps that often advertise private ride-hailing services?
  • Does real-time transfer information increase the willingness to transfer between routes?

To increase participation, the SFMTA promoted the survey through blog posts and email listservs and the Muni shelter signage.

Through a city-wide survey, we asked respondents to imagine scenarios where they were going home from work or school and had to wait 20 minutes.  Suspecting that few respondents would be willing to wait that long, we designed situational questions to determine whether different types of real-time information could effectively extend one’s willingness to wait.  We tested the following scenarios:

Scenario 1: Respondents arrive at their stop and see a digital sign predicting a 20 minute wait. 
Scenario 2: Building on the first scenario, the NextMuni sign suggests an alternate route three blocks away arriving sooner.
Scenario 3: Before walking to their stop, customers look at their smartphone and saw their wait would be 20 minutes.
Scenario 4: Customer plans their trip using Google Maps which also advertises TNCs such as Uber and Lyft.



Through surveying over 5,000 Muni riders, we learned that  more accurate and contextual real-time information has the potential to increase Muni ridership.

 In scenario 1, 44% of respondents took Muni either by waiting for the initial route or identifying an alternative route to their destination. When the SFMTA recommends a nearby alternative route, now 83% of respondents decided to stick with Muni. 73% of respondents also stuck with Muni when they could check a application for real-time information before traveling to their stop. Muni mode share decreases to 65%, however, when customers view predictions in an app that also features a TNC ad.