Tidal Patterns of Human Mobility in Bike Sharing Systems2016, HARVARD University
Every day, more than 6M user trips and 30 percent additional worker movements, shuffle continuously bikes in stations at 950 cities across the world. The vast amounts of data from this complex activity have brought scientists in front of challenging questions, not just about bike sharing, but most importantly, about the future of, more advanced, on-demand mobility systems like autonomous cars. Does sharing decrease cruising vehicles more than it increases parked vehicles? Are human mobility patterns random or predictable? To what extent does form of a city affect its mobility pattern? Are there similarities in behavior between MoD systems in different cities? We analyzed a dataset of 620K trips and 32M station updates from Boston?s bike sharing system during 2012. We discovered that even though trip patterns are random, their net effect on inventory patterns is stable and predictable. Independently of topology, users always move vehicles from residential to commercial areas in the morning, from commercial to residential areas in the evening, and from shortage to surplus areas throughout the day. For workers, it is the other way around. This palindromic tidal movement is consistent across systems, suggesting similarities in movement patterns across cities. Our discovery provides a new perspective in the regularity of complex behavior in on-demand shared systems. Studying human movement microscopically through discrete trips is as complex as studying water movement at a molecular level. Here, we study human movement macroscopically through its effect on inventory stock levels. To reconstruct the progress of the system in time, we first calculated local outflows and inflows per station per unit of time from the dataset of trips. Next, we integrated the dynamic trajectory that the system would have if no rebalancing took place by iterating over time and for each time step, for each station, deriving the inventory level by subsequently adding arrivals and removing departures. To visually reveal the hidden regular macroscopic patterns of commuting, we classified and ordered stations based on how residential, commercial, surplus, or shortage, their activity patterns are. To classify a station we compared the shapes of its departures (outflow) and arrivals (inflow) temporal distributions: The more skewed to the left the departures distribution compared to the arrivals distribution is, the more residential the pattern of the station is. The greater the volume of the inflow distribution compared to the outflow distribution is, the more surplus the pattern of the station is. This provides a common framework to study similarities in the dynamics across seemingly diverse and heterogeneous systems.