Data Driven Rebalancing of Citi Bike Operations

Visualizing dock stations and bike rebalancing

To aid our exploratory data analysis, we built the below tool to show station information. Hover over the stations to see station name, elevation, number of trips taken from that station in 2019, and number of bike rebalances that year.

You can also check out our python dash application for more map visualizations and our presentation slides.

Rebalance Operations

The number of trips rebalanced by Citi Bike increased with general ridership for the first few years after the 2013 launch in NYC. But, in 2018, it drops dramatically.

This coincides with the introduction of the Bike Angels program and illustrates just how effective it was.

Rebalances⁄Year	Trips⁄Year vs. Rebalances⁄Year

 Scroll to see the above side-by-side images

We also found that almost every bike in service was rebalanced at least once each year and almost every station was involved in rebalancing operations. See here.

We wanted to see where bikes were being rebalanced, so we faceted the data by NYC borough. The vast majority of trips are in Manhattan, so it is not surprising to see that the majority of rebalances are also there. Most bikes are rebalanced from one Manhattan station to another.

However, there is still considerable effort required to move bikes across boroughs. Below, we see a log graph of bikes rebalanced. Without the log transformation, Manhattan would dwarf all other boroughs.

We can see the same information visually. Here, we see that Manhattan has all of the top 100 stations by trips.

number of trips

And the the same is true for stations ranked by number of bikes rebalanced from and to that station.

from	to

 Scroll to see the above side-by-side images

Stations with the highest rebalance activity mostly serve commuters traveling from home to work. These home-to-work routes often have bikes being rebalanced in both directions. In the mornings, a high number of trips are made from the “home” station to the “work” station and so bikes have to be rebalanced in the opposite direction to maintain supply. In the evening, the reverse problem is faced by the Citi Bike bike operations team. We see the pattern exhibited in the chart below.

Seasonality

Prior to the launch of the Bike Angel program, bike rebalances followed a similar seasonal pattern to trip demand, with considerably more activity in the summer months. Post-launch, this seasonality still exists, but to a much lower degree.

We also found that the Bike Angel program reduced rebalance needs on weekends relative to the rest of the week.

Rebalance Timing

When does Citi Bike rebalance bikes?

The data doesn’t tell us when a bike was rebalanced—only when the next trip started after the bike was rebalanced. Therefore our rebalance timing estimates (in hours) reflect the maximum time frame in which the rebalance could have occurred. While not perfect, we will assume Citi Bike does not needlessly rebalance bikes and this data is fairly representative of actual movements.

Aggregate data shows bikes are frequently used shortly after rebalancing—confirming considerable effort is required to satisfy immediate demand. Longer gaps between rides suggest the bikes were rebalanced to re-stock stations during periods of low-use, such for known trip patterns like commutes, or to ensure less-used stations have bikes. We see this usage pattern in the graph below.

There was a considerable drop in immediate-use rebalances following the launch of the Bike Angel program in 2018. This may also be the result of improvements to the Valet Service, allowing a commuter station to “overflow” with bikes that are then moved in bulk overnight.

Station Elevation

We wanted to find out how station elevation plays an effect on rebalancing. While NYC is close to sea level, there still exist hilly areas that can be a pain to climb. Our hypothesis was that bikers generally do not want to bike uphill, so stations that are at high elevations would have more bikes taken out than coming back in. So, Citi Bike would have to lug these bikes back up to the higher elevation stations.

We found that, in aggregate, more bikes were rebalanced to higher-elevation stations than away from them. The opposite is true for lower-elevation stations. This suggests that bikers who ride downhill sometimes find other modes of transport for return trips, not wanting to ride the heavy Citi Bikes back uphill. With Citi Bike electrifying its fleet, this pattern might go away as riders use ebikes.

In the below graph, we see that the there are more bikes rebalanced to higher-elevation stations (the area under the right half of the distribution) than to lower-elevation station (the area under the left half of the distribution). Focusing on years before (2016) and after (2019) the launch of the Bike Angel program, not only are there fewer total rebalances as previously discussed, but also there is a change to the distribution—the curve flattens out and shifts right, indicating that a larger percentage of bikes are rebalanced to higher-elevation stations in 2019 than earlier.

Even a free trip isn’t enough incentive to pedal uphill for the Bike Angels.

Top routes for bike rebalancing

The vast majority of rebalance activity occurs between a small number of station pairs in Manhattan. This makes sense in the context of commuter bike usage, where there is a need for a constant flow of bikes away from destination stations to origin stations to keep up with demand.

Notice some of the top routes are just the reverse direction of each other. Visualizing bi-directional flow between station pairs further emphasizes how critical rebalance operations are between a small number of station pairs. If Citi Bike focuses on keeping these routes properly balanced, the majority of ridership demand would be satisfied.

We visualized the top station pairs for 2019. Hover over the arcs to see the net bikes rebalanced between a station pair (bikes_net = bikes_from - bikes_to). The arc thickness indicates the number of bikes rebalanced and color indicates direction.

Open this visualization in a new tab. Bikes are rebalanced from blue to red stations along the arcs

Click image to open visualization

We see two main patterns:

1. Bikes are moved from lower Manhattan to the Upper West Side along long routes. Ideally, Citi Bike rebalances these routes using vans.
2. There are a lot of station pairs clustered in the East Village. Some station pairs are just a couple of blocks apart, so Citi Bike can manually rebalance these stations or use bike trolleys.

When are routes rebalanced?

Investigating rebalance timing of the top 5 stations by number of bikes rebalanced reveals that not all are managed in the same way. Some seem to have the majority of rebalances done overnight while others seem to be more frequently rebalanced for immediate use.

If we focus on a single station, for example E 10 St & Avenue A, and look at its top rebalance routes, we can see there are distinct uses for each one. For this station, rebalancing from Cooper Square & E 7 St is typically done to satisfy immediate use demand, while rebalancing from E 14 St & Avenue B almost exclusively is done to fill the station during hours of low activity.

It should be noted that rebalance operations are probably more nuanced than bikes being moved along static routes involving only two stations. A van might stop at several stations along a circuit—picking up or dropping off bikes at each one. There may also be other critical decision points not obvious from an aggregate-focused analysis like ours.

However, combining our analysis with a time-series model, it could be possible to reverse-engineer Citi Bike’s rebalance operations and create a model predicting a station’s need for rebalancing. Additionally, using the real-time station utilization data from the Citi Bike station status feed, a more sophisticated, real-time model could be created.