CREATING THE NEXT GENERATION OF CUSTOMER INFORMATION IN SAN FRANCISCO

THE OPPORTUNITY

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.
 

MY ROLE

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. 

USER RESEARCH

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?

PLANNING & RECRUITMENT

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

QUALITATIVE RESEARCH

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.

QUANTITATIVE RESEARCH

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?

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.

INSIGHTS