Previously, we examined general ride-sharing prices across entire cities. Next we turn our attention to how pricing varies geographically across individual neighborhoods in San Francisco. You've probably observed that Surge and Prime Time vary based on where you're getting picked up; during the World Series you will likely encounter high Surge pricing in SOMA while your friend in the Mission is being quoted a regular fare.
We created an interactive map showing how frequently Surge and Prime Time appear in each neighborhood. Darker tones indicate a higher likelihood of encountering each. We’ve also listed the average pricing multiplier within neighborhoods to represent the severity of Surge and Prime Time. You can explore the data across several time periods: Morning Commute, Evening Commute, Weekday Evenings, Weekend Daytime, and Weekend Nightlife.
Explore the full-page map for the best experience, but you can see an embedded version below.
We can observe that, as expected, morning and evening commutes have opposite dynamic pricing patterns. In the morning, people from outer neighborhoods need to go downtown, so the demand in those areas is higher than normal. Most rides then terminate downtown, so it’s likely that driver supply ends up higher there, resulting in less frequent and severe dynamic pricing in that area. In the evening we see the opposite effect, with higher Surge and Prime Time frequency in downtown and less in residential neighborhoods.
Less intuitive is that on weekday nights after 7:00 pm, the frequency of dynamic pricing is far higher than we would have expected. Over 50% of requests in the central part of the city trigger dynamic pricing, often at high multipliers. We experienced this steep pricing personally when leaving work at 8:30pm on quiet weeknights, and this was one of the motivations for building What’s the Fare in the first place. A possible explanation is that while these are off-peak hours for demand, they are perhaps even more off-peak for drivers who perceive these hours as slow or less desirable. In the future, we believe that improved pricing transparency will allow drivers to discover regions and time periods that are unrealized opportunities and even out bumpy pricing patterns.
Results shown here are from requests to whatsthefare.com from September 8, 2014 to October 16, 2014. Note that we group results by neighborhood because that's the way that most people intuitively understand the city, but Uber and Lyft do not necessarily calculate dynamic pricing across the same geography and it's unclear how they set boundaries for Surge and Prime Time regions. Neighborhood boundaries used here are taken from DataSF. Note that data is sparse for some neighborhoods for the time periods we examined, and these are colored in light gray.