I had previously noticed a fairly dominant Bikeshare market between residential neighborhoods north of Massachusetts Ave. and the downtown area.  In fact, trips between what I called Mid-City, Mid-City North, and downtown accounted for more than half of all Bikeshare trips. Corey had previously noticed that the higher up a station is, the more likely it is that originating trips are headed downhill.  That makes sense from a physics point of view – from a higher station, most destinations are downhill.  But, I think the question is whether the city’s topography is adding potentially costly imbalance to the system? Is there a relationship between the sender/receiver status of a station, and the mix of uphill vs. downhill trips from it?

From a long-term perspective, it’s okay for a station to be imbalanced over short periods of time, as a downtown station is receiver in the morning and a sender in the evening.  As long as those demand markets are numerically in balance, one solution is to just add more capacity (docks and bikes) to be sure that supply can accommodate demand.  But if a station is not in balance over the long haul, and consistently sees its docks fill or empty without natural re-balancing from other users, it will require constant attention and re-filling by the crews and the vans, which is (presumably) expensive.

Corey graciously provided the (straightline?) distances and elevation changes between all Bikeshare stations, which I ran a quick a frequency distribution on in Access on all trips in 2011.  The system is flowing net downhill.  The re-balancing vans appear to be moving bikes uphill, on net.  60% of all trips are headed downhill (or level), and 40% are headed uphill. I put “net zero” trips in the downhill section, but these are

Capital Bikeshare is flowing slightly net downhill. 6% more trips went downhill than uphill (net, station-to-station).

The vast majority of trips do not go up or down more than 20 meters, or about 60 feet.  This may be because it’s difficult to cover much more elevation change on a short bike ride in Washington, but it is possible – some O-D pairs can involve as much as 80-100 meter change.

Of course, we only know the elevation of the station where the customer began and ended, not the actual elevation change they pedaled.  I think about this on my commute sometimes, as I glide down Capitol Hill towards downtown, only to pedal uphill again from the Mall.

So, the question is whether some stations are imbalanced because of topography, and if so, how badly.  Is there a consistent relationship between a station’s “balancedness” and the average elevation change of trips originating from it?  I did up a quick scatter plot, although I’m still not sure whether it’s answering the question:

"Balancedness" vs. Average Elevation Change in Capital Bikeshare stations, all trips 2011

To me, this chart is saying a few things, although I’m not sure of the right way to visualize this data (let me know if you want the source file to play with and re-display):

• The majority of stations were roughly in balance in 2011.  The big cluster of stations in the middle are stations where origins didn’t exceed destinations by much, and just as many trips went uphill as downhill over the course of the year.
• Topography and demand does seem to be causing imbalance at some stations, and the problem is primarily “sender” stations where trips mostly flow downhill – e.g. the top left quadrant.  The stations in this area are, to name a few, 16th & Harvard NW, 36th & Calvert NW, 14th & Harvard NW, Wisconsin & Macomb NW, Tenleytown, 11th & Kenyon NW.
• High-elevation stations are not necessarily imbalanced. Topography is not the only thing causing imbalance.  Look at the circles spread out along the x-axis – these are high elevation stations where most originating trips are headed downhill, but the are similar numbers of trips coming back uphill dock and balance out the station over the year.

Finally, here’s one last nugget – a distribution of station-to-station trip distances, all trips in 2011.  Of course we can’t tell the actual path people followed from origin to destination,but  these numbers are coming out similar to the duration numbers:

Station-to-station distance of Capital Bikeshare trips, all trips 2011. About half of all trips are 1.5 miles or less, and 83% are less than 3 miles - about the distance one can bike in under the 30-minute window of free time.

A few observations:

• Lots of short trips! Half of all trips are 1.5 miles or less. This is consistent with the finding of lots of rentals in the 10-15 minute time frame.
• There is no big difference between casual and registered users’ trip distances, and the majority (83%) are under 3 miles – also about what you can reasonably bike in the 30-minute window.

Spring Mill Bakery - mmm, bread...

#5: Any Store that is Not a Grocery Store. The other day I made a run out to my new favorite local bakery, Spring Mill Bakery on Barracks Row.  I’m working on sampling all their breads, because they’re always selling something new, and it’s always yummy. This time it was a loaf of dill onion rye.

Learned on this ride: Bread from a bakery beats a grocery store every time.

Total mileage: 2 miles round trip

.

.

.

Canadian geese swarm the Mt. Vernon Trail

#1: Work. Okay, I know I used this one already, but last weekend I had a couple of pretty interesting rides to and from work.  I was again out in a different location for work, but thankfully it was right near the W&OD trail, so I decided to ride.  On Saturday heading home I left Falls Church around 2:30pm, and had a very pleasant ride over the rolling hills of the Custis Trail, down to Rosslyn, and then on to the MVT.  The banks of the Potomac River were littered with Canadian Geese:Maybe I should have noticed that the geese were all hunkered down, because as I popped up to the 14th St Bridge, I noticed to my left that I couldn’t see the National Cathedral. It was shrouded in white mist. Odd.

Where did Georgetown go?

A few minutes later, Rosslyn has also disappeared. Then, it was suddenly snowing sideways.

By the time I got to the other end of the bridge, I could no longer see Georgetown, or Roosevelt Island.

Then, all of a sudden, it was snowing sideways as the squall pushed through.  The wind whipped up, temperatures dropped, and I had to brace my knee against my bike to keep the wind from pushing it over to take this photo:

Back on my bike, a gust shoved me so hard I lunged right and my knee ricocheted off the guardrail on the bridge. At that point I decided I wanted to avoid going into or across the wind as much as possible for the rest of the trip, so bailed on my earlier route over Capitol Hill, headed down Maine Ave. and through SW to home.  Brrr…

Winter sunrise over the Memorial Bridge

Then, the next morning, I headed out in all 23 degrees and was back out towards Falls Church. I was close to bailing on the bike altogether, but figured it’d be an adventure, and could always hop on the train if it was too slippery.  I went over the Memorial Bridge, and stopped to catch the sunrise (I really should watch these more often).  I had warmed up enough to shed layers.

Climbing up the Rosslyn hill

The trails were in okay shape, sometimes covered in a thin layer of snow, but the bike did fine. The bigger bummer was the incessant headwind, and all the over-salting on the roads, alas.  But, I got it back on the ride home, and had a niiiice tailwind pushing me home.

Saturday one-way: 13.1 miles

Sunday round-trip: 25.2 miles

Things I learned:

• Cycling in the cold won’t kill me.
• Winter sunrises really are quite pretty.
• Take it slow in the snow.
• Wind can be fun, but really strong gusts can be dangerous to cyclists perched on two skinny tires.

Snow in the fenders.

Ridership Over Time: I want to highlight from the comments a great visualization from Bilsko, showing ridership over time of day and year all at once. This “heat map” really shows how the “summer effect” on Bikeshare has as much to do with the pleasant temperatures, as the longer hours of daylight in the evenings. This is definitely worth a good look. Very cool!

Where Are All the Casual Users? Are Capital Bikeshare’s casual riders all taking long rides on the Mall, as the stereotype would lead you to believe? Are all the registered users using it to commute to work? Or are “casual” users really tourists, or just infrequent native users? To help shed some light, I mapped the percentage of trips originating at each station, by user type:

Bikeshare trips begun by casual users, by station. (click for larger)

A few observations:

• While the stations around the Mall, the White House, and Georgetown are unusually “casual” compared to the rest of the system, there are exceptions to that rule.
  • Casual ridership is relatively high in areas that aren’t your “typical” tourist destinations – Courthouse, Pentagon City, and the SW Waterfront, and two stations to the east of the river – near Fort Dupont at Minnesota Ave and Branch Ave, and at Pennsylvania and Branch Ave (although they’re relatively new).
  • Conversely, casual ridership seems low in areas where I’d expect to see more tourists – look at the swath between Farragut Square, Dupont, and U Street. Maybe that’s because there’s so much ridership in that area that the regular riders are simply overwhelming the casual users.
• Casual ridership may be high at new stations, as people try it out. As time goes by, people may sign up for the year, and the station turns colors.
• Look at how “local” the H Street corridor is (I live near there, so it catches my eye). Also, the area towards Petworth I called “Mid-City North” earlier – very local.

Where Is the Balance? It’s clear some stations are more imbalanced than others, over the long term – but how does that look on the map? Are low-lying stations net receivers, and hilltop stations net “senders”?

Net sender (red) or receiver (blue) Capital Bikeshare stations (click for larger)

Sure enough, the stations in Mt. Pleasant, Columbia Heights, and other neighborhoods uphill from downtown, are net senders. (Corey also confirmed that the average Bikeshare trip is net downhill.) Stations around the Mall are net receivers. I’ve shown this in raw numbers rather than as percent of total ridership, so the tan stations that look “in balance” may in fact be low ridership overall.

How Long Do You Ride? Whenever I see people on those red bikes, I wonder where they’re going. Is this a 5-minute dash to make a long-ish walk a lazy ride? Or is this a serious 25-minute haul? Does this pattern change whether you’re in Woodley Park, vs. Crystal City? How does the system serve different needs in different places? Here’s average trip duration, in minutes, by station of origin:

Capital Bikeshare trips by duration, by origin station (click for larger).

I had to exclude trips over 4 hours, since I’m working with a mean (not a median), and some very long trips were throwing things off.

• The Rosslyn-Ballston corridor is interesting – it looks like Ballston, Virginia Square, station further west near Clarendon stay local, with shorter trips. But at Courthouse and Rosslyn, average trip length goes up to 25+ minutes, suggesting they’re going across the Potomac River to DC. The “breakpoint” seems to be about Courthouse.
• The different trip lengths at Crystal City mystify me – any suspicion on what’s going on here?
• Interesting that the stations in Ward 8 (more or less) seem to be shorter than the stations in Ward 7. I would guess it has something to do with the speed and quality of the Anacostia river crossings, but I don’t know why trips from Minnesota Ave. area would be so much longer than those from historic Anacostia.
• The gap in stations on the Mall, plus the tourist ridership, seem to be driving longer trip lengths. I imagine this will drop this summer, as stations go in on NPS land on the Mall.
• I’m surprised at how short the trips are, even from neighborhoods north of U Street NW (“Mid-City North”). Trips from these neighborhoods seem to take less time than neighborhoods similarly further from downtown. Any ideas what’s going on here?

Okay, next time I want to delve into maps like this, but I realize I need to adjust for the length of time each station has been open. Stay tuned:

Preview of visualizations to come. Still need to adjust for length of time the station has been open.

Sligo Creek Trail, a hidden gem near Takoma and Silver Spring. This is the Carroll Ave. bridge, I think. (click for larger)

The ride took me on the usual route via Met Branch Trail and the Anacostia NE Branch Trail up to College Park for a certain work project.  The ride was uneventful, and the nice (for February) weather had alot of people and kids out on the trail.  No crimes on the MBT, and one confused driver at the intersection of the NE Anacostia Trail and 38th Street in Mt. Rainier.  As I was slogging into a headwind (they’re everywhere!) on the levee, the sun popped out, covering the river and the floodplain in light, it was really beautiful.

Back down the trail from College Park, I decided to try something different.  Rather than sticking to my original plan of going back to DC and taking R across downtown to the W&OD in Virginia, I decided to try the Sligo Creek trail from Hyattsville up to Silver Spring.  I knew of it, but had never tried.

The trail meanders, zigs, zags, crosses the creek many times, and there are beautiful views around every corner.  Don’t take this trail if you’re looking for pristine conditions on which to set your world speed racing record. Take this trail for a leisurely, scenic ride.  It’s a real gem, and still inside the Beltway too.

One Giant Loop

I rode through downtown Silver Spring, through 2nd Avenue, and then around to pick up the “future Capital Crescent Trail” where the trail emerges from an unused rail bed in an industrial area.  Heading towards Bethesda, the trail changes from packed dirt (sometimes mud) to nice, smooth stone dust and then pavement.  I was thankful for big wide Schwalbe tires.

Back on familiar ground at Bethesda, I ran the gauntlet on the CCT down to Georgetown. In keeping with the spirit of the ride, I schlepped over to the Virginia side to the MVT and tried the I-66 bridge back across to DC – only because I’d never done it before. I found it pretty narrow and tough, and cursed the highway planners of the 1960s when I found myself on the sidewalk amidst well-dressed patrons of the Kennedy Center.  Anyway, back up the Mall to home.

Looking at the map later, it does sort of look like a big loop, kind of like… a bike Beltway? Imagine adding the new trail by the Arboretum… sort of?

I any case, 43 miles total, and a great ride.

Sunrise over the Potomac River, on Memorial Bridge Feb. 4, 2012. Poofy red jacket in panniers - would prove useful later.

#1: Normally, my commute to work is all of 3 miles, from eastern Capitol Hill to downtown, and is not terribly exciting or long.  I sometimes get cold toes in the winter or sweaty in the summer, but since it only lasts 12-15 minutes, I can pretty much grin and bear it, as long as it’s not icy or snowing.

But last Saturday my commute changed – because it was a Saturday, which was weird, but also because I had to be in Courthouse at 8am, moonlighting (daylighting?) for a work thing where I try to be helpful and hand out brochures.

Anyway, I figured this was a good opportunity to record a “work” trip (#1) on the Utilitaire 12 since it was different than normal.  Things I learned:

• It’s cold before dawn in January!  It made me appreciate my normal post-dawn commutes.
• Taking Independence Ave. is a fast east-west connection across downtown, but it’s only advisable at off-hours.
• Winter sunrises are beautiful. Maybe I should wake up for them more.

Utilitiare 12.1 - To Work in Courthouse

The return trip in the afternoon was much warmer, but traffic was awake again so I reverted to the 14th St. Bridge.

Trip date: Feb. 4

Length: 15.2 miles round-trip.

.

.

#12: Okay, so I didn’t take a picture of this, but I did ride to my haircut today.  I have a map of the ride, if that counts?  I figure I’ve only got one shot at the haircut one during this game’s timeframe, but who knows, maybe I won’t need this one in the end.  Figured I’d write it down for posterity’s sake.  Note to self – take more pictures.

Ride to haircut (map lamely standing in for photo)

Usually, the biggest cost in a transportation network is labor. It takes non-travelers to operate and maintain cars, buses, and trains, and there’s little room for mechanical substitution. The majority of most transit agencies’ operating resources in America go to people, not capital. Intrigued by the idea that bike-sharing might reduce operating costs by letting other travelers move the system back towards equilibrium, I wanted to delve further into the role of “rebalancing” in the bikeshare system. I’ve heard a rumor that Montreal’s bike share system barely needs re-balancing, which intrigues me. Are the “peaked” demand patterns (temporally and spatially) in Washington driving the need for re-balancing and its costs?

We can look at the trip-making patterns in the system and try to understand how far off the system is from being inherently self-balancing. When a station shows big net differences between trips supplied and received, we can guess it’s re-balancing.

So, are certain times of the day more “senders” to the next hour than “receivers”? Between 8:00 and 8:30am, how many bikes are taken out vs. returned? In other words, what are the most popular hour-boundary lines for rentals to cross? For each half-hour chunk of the day, do more trips begin (net “sender”) or end (net “receiver”)? It’s a bit arbitrary, but since most trips are 30 minutes or less, I divided the day into half-hour chunks, 2011 only:

Which half-hour chunks are net Senders/(Receivers) of trips? It's a little arbitrary, but it does shed light on usage patterns over the day.

This shows that, for example, between 8:00 and 8:30 am, 7,000 more trips began than ended in 2011. In fact, from 7:00 to 8:30am, the system is still getting going – the rate of change is going up, and more bikes are out than were before. Between 8:30 and 9:30 am, the trend reverses and the system has started to trend back towards more bikes docked than active, and it stays that way ’til 10:00 am. Midday is inconsistent, but shows some lopsided activity in the 10-11:30am timeframe – maybe this is tourists taking out long rentals? Remember that systemwide there’s a “mid-morning lull,” but this chart suggests there’s a net out-flow of bikes leading up to noontime.

Again, this is a bit arbitrary since a trip from 8:50am and 8:59am would show up as net zero in the above graph, but a trip from 8:50 am to 9:01 am would show up as +1 net sender for the 8:30 time and -1 net receiver for the 9:00 am timeframe. But, these raw numbers end up being 10-30% of all trips begun in a 30-minute window, so we’re not talking noise here, and it gives you a sense of the net inflows and outflows of bikes.

A huge chunk of people take out bikes in the 4:30-5:00pm hour, and return them after 5:00pm. Then, in the evenings, the system is on net trending slightly downwards in activity from 5:00pm onwards. (BTW math geeks – is this kind of the derivative (rate of change) of overall trip-making? Calculus was many moons ago for me).

I imagine this pattern is markedly different on weekends (note the smaller scale on this graph):

Capital Bikeshare Net Trips Supplied or Received by Half-Hour, Weekends Only, 2011

More interesting would be to see where the net in-flows and out-flows are occurring. So, I took the graph in Part 5 and broke it down by net trips sent/received by zone (zones defined in Part 4), and by time of day – before noon and after noon.

Capital Bikeshare Net Trips Supplied or Received, by Zone

This one is pretty interesting – for example, you can see that Downtown is a huge net importer of trips in the morning, and exporter of trips in the afternoon/evening. On the whole, it’s a net receiver of trips, which we saw in Part 4, this shows the imbalance over time as well. Mid-City, Mid-City North, and Capitol Hill are all net “senders” of trips in the morning, and the trend reverses in the afternoon. The Mall is more like downtown. Other places like Rosslyn-Ballston, Crystal/Pentagon City, SW/Near SE, and upper NE and NW look more in balance, although their overall level of activity is lower (Rosslyn-Ballston, remember, is a relative newcomer in 2011).

But how does this metric of “balanced-ness” compare to the total trips in and out of these zones? I mean, downtown looks pretty imbalanced over time in the above graph, but there’s also a huge number of docks and stations in downtown, so it’s not necessarily a problem. Here’s total in-flows and out-flows before and after noon, plus the total number of originating trips in each zone, to put the sender-vs-receiver numbers in context:

In percentage terms, actually Mid-City North (Petwork, Columbia Heights, etc.) and upper Northeast is more off-balance than downtown. Capitol Hill isn’t far behind. These neighborhoods generate significantly more trips in the morning, but don’t receive an equal amount in the afternoon to compensate.

Here are the top 10 “supplier” or “sender” stations, before and after noon. Interestingly, Lincoln Park is the single biggest net “sender” of trips in the morning, meaning it is the single most lopsided station in the mornings in 2011. This stations is followed quickly by several stations in the Mid-City neighborhoods – U Street, Logan Circle, Adams Morgan, etc. In the afternoon, the most lopsided stations are downtown, especially in federal office areas where there’s little activity at night. However, I was surprised to see the uneven-ness at Foggy Bottom and 14th and G Streets NW – any idea what’s going on here?

Capital Bikeshare Top 10 Sender Stations Before and After Noon

From a financial sustainability perspective, it’s probably okay for a station(s) to show an imbalance over the course of the day. The question is whether the station or area gets full or empty before the tide begins to change back the other way, and balance is restored without the cost of a re-balancing team.

What do you think? What’s the end-game for financial sustainability, given the clearly “peaked” patterns of demand in Washington? How peaked is the demand really, unconstrained? Is that a useful thing to measure?

Flags in the Wind - Washington DC by Glyn Lowe Photos, on Flickr (click for link)

The other day I was headed westbound down the Mall on Madison Drive, when I stopped next to a fellow cyclist at the light at 14th.

Me: “Isn’t this weather great? And finally, no headwind!”

Him: “I’ll say, it’s a nice change.”

Me: “It seemed for awhile there we had headwinds every day.”

Him: “You know, I had a cyclist friend who used to say, ‘For every uphill, there’s a downhill, and for every headwind, there’s a headwind.’”

I realized that this metric of “balancedness” (need better word) of bikeshare stations can be sliced and diced across a few different dimensions.  Here’s the level of balance by station in 2011.  At first it appears that many of the most popular stations can be the most off-balance (on net, over the year), but this could be just that there’s more trips at the busier stations – this need further research, and I need a better measurement of balance.

Anyway, how about adding time of day and seasonality to this? Are some stations way off balance in the morning, but it reverses again in the evening?  Does a station’s balancedness change over time of year?  Anyone want to take a crack at this?

The net "balanced-ness" of Bikeshare stations over the course of 2011

So, where are the Bikeshare bikes going to, and from?  While we can’t tell people’s final destinations, we can tell what docks they use.  So, I did a quick look at usage by dock, but unfortunately, with 142 stations, it’s impossible to visualize cleanly at the station level:

Can you make any sense of that? I can't really either. This is total trips by station. Time to aggregate.

But, I will say it’s interesting how the most popular station at Dupont Circle is the winner by a long shot.  As has been discussed elsewhere, this is likely because there are few other stations around, it’s a big station, and it was one of the first installed.

Dividing Into Zones: I divided up the Bikeshare stations into 12 big groups, kind of like “super neighborhoods,” to get a sense of where the riders are going to and from.  This is more art than science, but I basically drew lines based on my knowledge of biking around the city, and a healthy dose of arbitrariness.

I divided up the Bikeshare stations into these zones to make it easier to see trip patterns.

You can argue with how I drew the lines, and how I had to wing it when a station was right on the line, etc.  But I just needed some rough groupings of stations to make sense of travel patterns.  Wards seemed a little too blunt, e.g. Ward 6 encompassing everything from Shaw to the Ballpark. So, I drew Downtown as roughly the CBD from Constitution Ave. to Mass. Ave., Mid-City as everything in the middle south of Woodley Park and U Street over to Union Station, and then the others kind of fell in mostly logical groups.  I put the station at L’Enfant under Mall, and Woodley Park under Upper Northwest – eh, it was a judgment call. I also totally made up the zone names. The main thing is, we can now look at a manageable amount of numbers.

The numbers shook out so that every zone had between 4 and 30 stations, with most about 10-20.  More on that later.

Origin-Destination Matrix. Interestingly, this produces an 12×12 origin-destination matrix, or 144 different possible combinations of origins and destinations.  Here’s the full table, just showing 2011 trips, if you’re interested:

Capital Bikeshare origin-destination matrix by zone. Unpopular trip choices colored blue; medium and high colored white and red. (Click for larger)

Now, a few observations with this stuff.  A caveat – I haven’t accounted for the fact that stations were added over time, so neighborhoods that first got stations installed are going to look more popular than more recent arrivals, such as Rosslyn-Ballston.  And, different zones have different number of stations – although more station could be a symptom of demand just as much as cause. Nevertheless:

• Trips to and from downtown dominate bikeshare patterns, at one third of all trips in 2011.
• Trips tend to be short, as we saw earlier, making inter-zone trips popular (that’s the diagonal line running from top left to bottom right.  Especially in Capitol Hill, Mid City, and others.

Capital Bikeshare Top Travel Markets, 2011

I halfheartedly attempted to draw this table on a map, with admittedly third-rate arrows in PowerPoint.  It’s a little clunky, but it does (sort of) give a visual representation of the table above.  For this illustration, I suppressed all flows less than 30,000 trips or so.  I am clearly not a graphic designer – anyone want to help make this look better?

Capital Bikeshre trip patterns by zone, flows of 30,000 trips/yr and above. Thicker, darker arrows mean more trips.

In Balance, or Net Sender/Receiver? From the zone totals, I think you can also tell whether a zone is more or less in balance over the year (same number of trips beginning and ending), or whether it is a net “sender” or “receiver” of Bikeshare trips. If more trips ended at a zone than began at it, I believe the difference is the re-balancing crews.

Some zones are more or less in balance on net, while others are net senders or receivers of bikes.

So, over the course of 2011, 5% more trips ended downtown than began downtown, meaning that the rebalancing vans, on net, took bikes away from downtown.  Contrast that with neighborhoods to the north and west (places like Cleveland Park, Petworth, Mt. Pleasant, and others in Upper Northwest, Mid-City North) where it’s mostly downhill to downtown, and more trips began than ended. Here is the final column, graphed in terms of absolute trips:

Net trips supplied/(received) by Zone in 2011, i.e. trips originating minus trips ending. Some station groups are net trip "sources," others are net "sinks," and some are more or less in balance.

Okay, that’s all for now. Until next time, keep the comments and reactions coming! It’s nice to hear your thoughts.

P.S. – one further thought on the “balanced-ness” (is that a word?) of individual stations.

When you get on a Bikeshare bike, what’s that bike’s history? Has it been all over town in recent weeks, or are you the first to use it in awhile? How evenly is each bike in the fleet used? Do some bikes do all the work, while others sit?

The dataset of all trips counts 1,343 unique bicycle ID numbers. I’ve excluded 25 bikes that have strange ID numbers (they look like “? (0xEB7B5641)” if you’re interested), and suspicious looking activity, but they only represent 0.3% of all 1.36 million trips.  Here’s how the remaining fleet of 1,318 bikes breaks down by number of trips over 2011, excluding the 2010 growing pains. It looks like Bikeshare introduced about 200 new bikes to the fleet in late October 2011 (the ones with the flashing LED front lights?), so I’ve called those out separately.

Capital Bikeshare Fleet by Number of Trips. Most bikes were used 1,000 to 1,500 times in 2011, but some were used much less.

So in 2011, most bikes in the fleet were ridden between 1,000 and 1,500 times. No bike was ridden more than 1,600 times. Interestingly, a bunch of bikes were used at half, or even a quarter as much as the average bike. This could’ve been because the bike was pulled from rotation in the winter when the fleet is reduced, or for maintenance reasons.  I can’t tell when a bike was pulled for maintenance, but I can exclude winter.  Here’s all trips in 2011, but from April to October only when the fleet was presumably in near-maximum service (new fleet not shown here):

Capital Bikeshare Fleet by Number of Trips, High Season Only.

All the numbers come down since we’re only looking at the high season, but still a fair chunk of the fleet seems to be getting used alot less than average. What’s going on here?

Are bikes getting stuck in less popular stations?  How completely does the system churn? Are there pockets of bikes that get stuck on the back bench for awhile, only to hit the big time weeks later?

One way to look at this is to say – Of the bikes with only 300-800 rides in 2011, were there lulls in usage? What station were they last docked at before a lull, and is there a pattern in those stations? Do those stations correspond to the usage numbers by station?  I’ll need more time to set up that query – anyone want to take a crack at it?

One parting thought – what percent of a Capital Bikeshare’s life is spent sitting at a dock?  I get something like 415,000 hours of total usage in 2011 (bike-hours, if you will).  Each year has 8,760 hours in it, times say 800 bikes in service, and that’s 7 million bike-hours of total availability.  So each bike is being ridden 6 or 7% of its waking (and sleeping) hours, while the other 90+% of its life is spent on the dock? Is that low or high? How does that compare to a typical car, or bike?