With any system, every potential member has a price he or she is willing to pay for the service provided. When deciding whether to purchase an annual bikeshare membership, customers often have only one option: the list price, unless a special discount applies. In essence, system operators try to balance two variables: appropriate revenue for the system to cover its costs, while serving the general public with an affordable transit option.

When setting price, system operators must have an understanding of what potential customers will pay for their service. Those who value the service highly would often pay more than the list price, while those with better alternatives would pay significantly less for the same service. Because we have limited data on customer preferences (without issuing a survey or having other transit data), we can start by analyzing the general needs of particular transit groups, and estimate an average cost change over the current mode of transit. The end goal will be to understand how variable pricing can increase the number of annual bikeshare members.

Each mode will be ranked individually (grouped by transit mode) with these parameters:

1. Willingness to pay (demand) – low, medium, high
2. Cost change (-/+) over current mode

The chart below is the end result, after the analysis has been complete. Later in the article, I’ll discuss each transit mode to justify the rankings.

With this ranking, we can now try to plot this to desired price-points along this spectrum. Again, we don’t have hard data yet, so let’s simply try to anchor the upper and lower bounds.

On the low end, imagine a single parent with several young kids whom all need transportation to school; she may like the idea of bike sharing, but her needs are simply not fulfilled by the service – we’ll set her willingness to pay at $20/year. On the high end, imagine a young male who lives downtown and his primary mode of transit is walking/busing and doesn’t want to own a car or his own bicycle – we’ll set his willingness at $130/year.

The rest of the points in the range will now be interpolated through our rankings:

Another reference point we add in the average full price, based on prices from Boulder – $55, Denver – $80, and DC – $75.

Now that our framework is built, I’ll provide further justification for these rankings. Moving from the least ideal demographics to the most ideal, we’ll discuss each transit group’s incentives to use bikeshare and their potential costs.

Least-ideal demographics

family vehicle, L++

Families usually need flexibility, including a method to transport younger children – an automobile is the most common choice. If these families use bicycles, they will often purchase bikes that are capable of transporting children instead of using bikeshare, which can only transport one person each. Bikeshare will be an added cost over a car and/or other commuter bicycles, and may not meet their needs.

bike commuters (non-bikeshare), L+

This group is paradoxical: while bike enthusiasts are often the most vocal and supportive of bike sharing, they have a low potential to pay for or use the system since many already commute by bike. Only the most earnest supporters will buy an annual membership – more as a gesture of community support rather than for direct personal benefit. One positive benefit bike-sharing provides is against theft. With traditional bike theft is so hard to stop, bike sharing is a win for both the user and the system. Anti-theft is a good selling-point, but often not a sole reason bike-commuters would opt for bike share. Bikeshare will be an added cost over their commuter bike, with limited upside.

carpool/vanpool riders, M+

Carpool and vanpool riders often use this mode of transit to save money – particularly on the capital cost of owning an additional vehicle. This group may positively gain daytime flexibility of bike sharing when individuals might not have access to a vehicle or as an alternative to using a bus/metro. However, carpool/vanpool riders are more commonly a small demographic, and often commuting from suburban areas, which would limit their use to mainly work/weekday hours. This group of people may be willing to pay a reduced price for bike-sharing, but likely won’t have enough benefit to justify a full price.

Key demographics

single-occupancy drivers, M-

Single-occupancy drivers are numerous and potentially the most desirable customers: they use significant road and parking resources, are less fuel-efficient, and spend a great deal of money to own and operate their vehicles. However, the barriers to entry are harsh; often these drivers are often not comfortable with biking/alternate transportation, and typically have the means to pay for the convenience of car use, and thus choose not to give up the flexibility of the car.

Reducing single car use is an important goal for environmentally sustainable communities, but one that’s difficult without significant assistance from local and regional governments and shifting culture. Other strategies may involve promoting bus/carshare along with bikeshare to fully replace the convenience of owning a car. Many single-occupancy drivers have already paid the fixed cost of car, and may be reticent to invest in other options while the vehicle still has many years of life.

walking, H++

Walkers (and those particularly without bikes) are ideal candidates for bike sharing. According to research from Walkscore, the ideal range for walkers tends to be one mile or less. Using bike sharing, a walker could easily extend to longer trips. This Capital Bikeshare data analyzed by JD Antos shows that 83% of trips are under three miles, and up to seven miles – an order of magnitude increase over the typical walking range (and likely faster). Another bonus: walkers will have fewer barriers in walking to a bikeshare station.

However, walking is a thrifty activity. While some may choose to walk for enjoyment or relaxation, others may necessarily walk due to the expense of other forms of transportation. Fortunately, bike sharing is an inexpensive option for consistent commuting and likely worth the price/time savings. Bike sharing can serve as important service for those who cannot afford other options.

bus/metro regionally, H+

Regional busing provides important longer distance travel, but often has limited transit options outward from the designated bus stops. Similar to car/van sharing, regional buses often travel a significant distance, but often leave the rider still some distance from where he or she needs to go. Bike sharing can help greatly to close this last mile gap in reaching a destination.

One newer innovation in bike sharing from Bcycle allows for free sharing of annual passes between two regions – Boulder and Denver, Colorado, for example. This is an exciting development as it can close the last mile gap on both ends, while also providing greater value for using the service in either area. Connecting bike sharing programs is a huge win for residents of these cities, and adds a tremendous value to the region. Customers who can take advantage of multiple regions will also be willing to pay more for this service, raising the average revenue.

bus/metro locally, H=

Those who often ride bus/mass transit should be prime targets for bike sharing as a more flexible and functional option. Bikeshare’s main advantage is its asynchronous operation, allowing a user to check out a bike at nearly any time, while buses have specific time schedules or limited service hours. In the case of light rail/subway, the same rules apply – and these services may be a relatively expensive option for some riders, while a bikeshare membership will be only a fixed cost for the customer. Bikeshare may also provide quicker and easier access to areas that a person may need to go. And lastly, bus/metro users want to take one leg of a trip using bikeshare, and return using the bus/metro.

When adding up all the benefits of pairing bikeshare with multiple alternative transit options, the sum is much greater than its parts, making a bike sharing annual membership a welcome addition.

Bridging the price gap

My hope from this article is to provide a method of viewing potential customers in a more descriptive and useful way, using some of the incentives we know about each population. More data will be needed to make judgments on each group’s willingness to buy an annual pass from changes in price or offering targeted discounts.

When we combine this with the concepts from my previous article on optimizing usage,  the next logical step will be determining what amount of discounts may be offered, how much the average rider will use the system, and how discounts can be justified relative to the fixed and variable costs of running a bike sharing program.

@ericmbudd

Great user experience for a bike sharing system has many components, including station location, ease of use, bike quality and availability. Specifically to bike operation and availability, two real factors apply:

1. the start station with adequate bikes (influenced by a. total bikes in use, and b. bike distribution)
2. the end station has adequate docks (influenced by a. total bike-to-dock ratio, and b. bike distribution)

The focus of today’s analysis will be on how bike sharing user experience changes with increases in total bikes in use (up to peak usage), while also considering the bike distribution among stations (as some stations tend to have a net-positive or net-negative flow which requires re-balancing).

Using data from Capital Bikeshare in Washington, DC (a large, urban bike sharing system with over 1500 bikes and 165 stations), we can begin to identify a case for maximum bike usage – both under an equal distribution of bikes, as well as under a more likely distribution of bikes (considering the balance under a natural station flow). The dataset used will be used from 1222 bikes (2443 docks) with 147 stations in the final two weeks of Q1 2012. The average number of bikes is determined by halving the number of total docks available, as we know that Capital Bikeshare uses a 50% bike-to-dock ratio. Based on this information, we can generate a model that simulates how many bikes the average station contains when x bikes are in use (i.e. not at any station).

Nominal operation

Here’s how the graph looks when no bikes are in use – the minimum number of docks at a station is 10, while the maximum is 39 (and will first model them at half capacity):

Next, we’ll use this as a baseline and steadily increase the number of bikes in use, decreasing bikes from stations at an equal rate. By maintaining our first goal of user experience, that the start station has adequate bikes, we must not let the peak rise beyond a level where the smallest stations no longer have bikes. For Capital Bikeshare, the smallest station is 10 docks; as it is an outlier, we’ll optimize for the many stations that have 11 docks:

Once we reach 662 bikes in use, we now see that stations with 10 or 11 bike capacity have less than 1 bike per station, on average – a total of 44 stations without bikes. Although the system still has 560 bikes available, there’s now a possibility that a new user will come to a station with no bikes available.

Station outflow/inflow

The above “average” case needs to also be viewed as the best-case scenario: bikes are evenly distributed, and new users check out bikes at an equal rate among all stations. However, bike sharing system rebalancers know better – some stations tend to always have a surplus, and some that tend to have few bikes, if any. We can model this by showing the total in/out data from the Capital Bikeshare system for the same period (knowing that any rebalancing does not reflect in this data):

As you can see, the graph of station flows are roughly 30% on each end that require rebalancing (+/-), and about 40% that mostly rebalance themselves. Interestingly, some stations have opposite trends on weekdays vs. weekends.

Typical operation

The modeling difficulty now: how can we apply this flow data to our static model of averages? My method is to take these measured flows (two week period) and divide them into a 12-hour average (which could be tweaked for more/less effect), which I then add to my baseline. Here’s the new graph, sorted by bikes available under typical use, starting at zero bikes:

Even with no bikes in use, some stations are empty or near empty. Using this new baseline, now we’ll assume the same previous number of bikes in use (our potential optimal maximum), 661:

With 661 bikes in use, we now show 40 stations without bikes – as compared to the nominal model, which only found one station without bikes. Some stations are deeply negative, which suggests even more demand for bikes (i.e. unhappy users who can’t check out a bike at the station they want).

A system operator must understand the potential trade-offs between increasing levels of ridership and the cost/ability to rebalance. This last graph helps answer that question, with its output being the number of stations that have less than one bike as the concurrent riders increase:

Using this data, a system operator can then make educated decisions for a target peak ridership (in order to maximize revenue) and what level of service customers should expect at peak system use. The cost of having peak bike usage less-than-optimal will be to forgo revenue or reduce community benefit; the cost of higher-than-optimal usage being increased or unmanageable rebalancing, or degraded user experience for the typical customer.

@ericmbudd

 

Read the first article in this series: bike sharing usage patterns in Washington DC’s Capital Bikeshare