What can we learn from ridehail data?

In less than a decade, ridehailing services such as Lyft and Uber have revolutionized how people travel, providing more than 12 million trips per day all around the world. Yet ridehail companies have largely kept policymakers in the dark about who exactly is taking these trips and where they are going.

Despite intense interest in the impact of ridehailing on the broader transportation landscape, companies have not shared their user data publicly. As a result, we do not yet fully understand how this new mode serves different neighborhoods and travelers, and who, if anyone, is being left behind.

In my doctoral dissertation at the UCLA Institute of Transportation Studies, I investigated ridehail access and equity in Los Angeles. My findings suggest that Lyft and Uber are actually reducing racial and ethnic disparities in travel and providing more auto-mobility in neighborhoods with less reliable access to personal cars.

Part of my research involved an equity audit study that found that Lyft and Uber have significantly reduced the effects of discrimination against black riders compared to the taxi industry, where racial bias produces stark service disparities between black and white riders. The findings were covered extensively in the press and, I hope, will motivate a reckoning for policymakers and taxi operators.

But in addition to examining ridehailing’s impact on equity and access at the individual level, I also obtained trip-level data from Lyft to perform the first academic analysis of ridehail travel, use, and equity at the neighborhood level. The data showed that Lyft travel is remarkably ubiquitous.

Between September and November 2016, 6.3 million Lyft trips reached nearly every part of Los Angeles County, serving neighborhoods home to 99.8 percent of the county’s population. There was no evidence that neighborhoods are systematically excluded from Lyft service based on the racial, ethnic, or economic characteristics of their residents. And a strong association between Lyft use and the level of household vehicle ownership in a neighborhood suggests that Lyft use is highest where its substitute — the household car — is scarcest. Trips on the shared, cheaper Lyft Line service made up 29 percent of all Lyft rides in LA County and were taken at a higher rate in low-income neighborhoods.

Lyft use was lower in predominantly Asian and Hispanic neighborhoods, suggesting either that car access without ownership is already being met through carpooling or informal services, or that barriers — such as lower access to smartphones, data plans, or banking — inhibit ridehail use. Most ridehail users traveled on Lyft only occasionally with the exception of a small group of avid users..  While 40 percent of users made less than one trip per month, another 10 percent completed more than half of all Lyft trips to, from, or within LA County.

Ridehailing extends car access across a wide array of built environments and particularly in neighborhoods with lower access to personal cars. There are still obstacles to access that Lyft and Uber could mitigate to extend access to travelers without smartphones and bank cards. Partnerships with community organizations to donate phones, or the creation of landline-based services, could allow more travelers to hail rides. Integrating ridehail payment with transit cards may extend access to un- or under-banked residents.

These data provide valuable insights into the use of ridehail. And it would undoubtedly benefit cities and policymakers to have access to similar data in the future. Without data, cities will continue to struggle to understand where and when people use ridehailing and how it fits into the existing transportation ecosystem. Ridehail data requests should be specific and connected to actionable performance metrics or mobility outcomes to ensure cities are asking and answering questions that can inform policies to promote more equitable ridehail access.