![]() ![]() Where you go: a picture of interconnectednessĬlustering demand for mobility over time gives us a picture of how demand varies in time and space. In April 2021, JFK and LaGuardia airports are prominent clusters, representing the recovery of airplane traffic in April 2020, JFK represents a faint cluster whereas LaGuardia is not even visible on the map. In addition to the difference in intensity indicating the drastic difference in overall demand, the clusters highlight changes in demand patterns. For instance, the construction of the Civic Center BART station in downtown San Francisco divided the mid-Market neighborhood in critical ways that remain to this day, 40 years on.ĭemand in portions of Queens and Brooklyn, in April 2020 (left) and April 2021 (right). That theory would be consistent with work noting that transportation networks - particularly postwar highways - split up and divided historic neighborhoods in permanent ways. ![]() Cluster boundaries hew close to arterials - suggesting that major urban roads tend to break up underlying patterns of economic and social activity. More notably, the clusters follow not just neighborhoods, but arterials as well. The SFO and OAK airports are also clearly visible, representing tight pockets of passengers requesting airport pickups. In many areas, the demand clusters faithfully represent actual neighborhoods, such as the Mission, North Beach, and downtown Oakland. By fitting a mixture model to the locations of rider sessions, we can obtain probabilistic clusters that highlight patterns of demand. We begin by examining the geographic distributions of rideshare demand. Where you start: pickup locations and a picture of fluctuating demand ![]() Here we showcase two different ways of thinking about how these patterns emerge from rideshare demand, and how those patterns in turn reflect the way we live and move in American cities. For instance, Manhattanites stereotypically loathe leaving the island: but is that true for Lyft riders? Does Lyft mobility show us that we do a lot of rides that start and end on the island, but rarely venture to the uncharted regions of Brooklyn or Queens? We’ll notice, for instance, that demand in an office district clusters heavily towards the end of the day while demand in a residential area may vary based on weekdays (mornings, for the commute) versus weekends (evenings, for going out).īut if we link pickup and dropoff information, we start to uncover patterns of connection between different parts of the city. If we choose only pickup information - both where a trip starts, and when - we get a picture of the demand for mobility. Lyft rides paint many different pictures of urban mobility depending on what part of the trip we emphasize. Two visions of mobility: where you start, and where you want to go This paints a rich picture of how we live in time and space, and helps to shape a complex understanding of the transportation services that modern metropolitan areas need to function socially, economically, and culturally. But, strikingly, we also see how cities resemble each other - that sometimes, common patterns, like urban downtowns, look more like other cities’ downtowns than they do their companion suburbs. That diversity implies a need for a diverse range of products and services. We see that cities vary a lot internally in how people travel, where, and when. Here we share what we can learn from long-run patterns on Lyft’s operations in major US cities. When and where they do so tells us a lot about urban mobility - whether and how the notions of neighborhood, geography, and landscape shape how people move through space. Lyft’s riders use our services to get to and from work, go out to dinner, visit family, and get to the airport. But where those people move, and why, is up to them. Lyft moves people through space and time. From: Alex Chin, Michael Jancsy, Shilpa Subrahmanyam, and Mark Huberty ![]()
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