Category Archives: Bike Share

Citibike beating Barclays Cycle Hire

NYC Citibike’s meteoric rise continues – for October, the New York City bikeshare beat London’s Barclays Cycle Hire on average journeys per day, for both weekdays and weekends. Even more impressive considering that it’s only just over half the size.

Thanks to this release published today at the London Data Store, and this daily updating data from New York, I’ve been able to plot month-by-month figures, for the last three years for the Barclays Cycle Hire, and the last few months for Citibike, on the same graph. I’ve split out weekdays and weekends. Grey/black is New York City’s Citibike, while the colours (red, orange, green, blue) are the Barclays Cycle Hire for 2010, 2011, 2012 and 2013 respectively.

bch_numbers_monthbymonth

Click for the large version.

What’s even more impressive is that Citibike is currently physically smaller than London’s Barclays Cycle Hire. It currently has 330 docking stations and 4500 bikes, while London has 558 docking stations and 7600 bikes. These numbers don’t match exactly with official numbers, as I combine a small number of adjacent docking stations, and don’t count bikes in repair or otherwise unavailable for use.

London’s more temperature climate (a “warm/cool” city) means it should have a lead on NYC (a “hot/cold” city) in the summer and winter, while NYC may well be strongest in the spring and autumn.

Apologies for the rather lame looking graph. Excel crashed as I was setting it up, I sneaked a screenshot as the crash reporter popped up, but had to add the NYC data in manually in GraphicConverter…

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Tracking, Visualising and Cycling

Along with Martin Zaltz Austwick, who blogs as Sociable Physics, I led a workshop session as part of CASA’s annual conference. The topic was “Tracking, Visualising and Cycling” and focused on analysing and mapping bikeshare data. I concentrated on mapping the near-real-time docking station data, while Martin graphed journey data. Both of us used Google Drive as a quick an easy platform to map spatial data and graph it. The techniques that the participants were led through are relatively rudimentary, but hopefully acheived our main purposes of demonstrating the availability of such data and the utility of Google Drive for quick analysis, without leaving anyone on the course behind.

After short presentations by Martin and myself, presenting our recent related output, there were two practical sessions. In the first session, I led participants through downloading the live dock locations/status JSON data files from bikeshare systems in the US, before hacking the JSON into a CSV suitable for upload to Google Drive and showing on a map as a Google Fusion Table. A calculated column was then added to show the empty/full ratio and the docking stations on the maps were coloured appropriately. The result looked a bit like this (if the New York dataset was picked):

trackingworkshop

A couple of gotchas we ran into: (1) If using Notepad, don’t save the JSON text, as that will “burn in” linebreaks that break it. (2) If you don’t see Google Fusion Tables in your Google Drive apps menu, you need to add it as an app using the button at the bottom of the popup.

Martin then followed by showing participants how to download journey data from the Washington DC “Capital Bikeshare” website, extracting just the data for Saturday 30 June 2012, extracting the number of minutes each journey took in Excel, binning the journeys by minute and then plotting it on a Google Speadsheet chart. An additional section was breaking down the plots by user type – showing a pronounced difference between Subscriber and Casual hires – the latter generally taking much longer for their journeys.

You can view the slides here.

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Analysing “CitiBike” in New York City

The above interactive map compares the popularity of different CitiBike docking stations in New York City, based on the number of journeys that start/end at each dock. The top 100 busiest ones are shown in red, with the top 20 emphasised with pins. Similarly, the 100/20 least popular ones are shown in blue*.

CitiBike is a major bikesharing system that launched in New York City earlier in the summer and has been pulling in an impressive number of rides in its first few weeks – it regularly beats London’s equivalent, whose technology it shares, in terms of daily trip counts, even though London’s system is almost twice as big (compare NYC).

Different areas have different peak times

Here are three maps showing the differences in the popularity of each docking station at different times of the day: left covers the “rush hour” periods (7-10am and 4-7pm), the middle is interpeak (10am-4pm), the domain of tourists, and on the right is evening/night (7pm-7am) – bar-goers going home? The sequence of maps show how the activity of each docking station varies throughout the day, not how popular each docking station is in comparison to the others.

nyc_rushhour_small

Red pins = very popular, red = significantly more popular than average, green = significantly less popular than average. Binning values are different for each map. Google Maps is being used here. See the larger version.

Some clear patterns above – with the east Brooklyn docks being mainly used in the evenings and overnight, the rush hours highlighting major working areas of Manhattan – Wall Street and Midtown, and interpeak showing a popular “core” running down the middle of Manhattan.

The maps are an output from the stats created by a couple of requests for CitiBike data came through recently – from the New York Times and Business Insider – so it was a good opportunity to get around to something I had been meaning to do for a while – see if I can iterate through the docking station bike count data, spot fluctuations, and infer the number of journeys starting and ending at each docking station.

I was able to relatively quickly put together the Python script to do this fluctuation analysis and so present the results here. I can potentially repeat this analysis for any of the 100+ cities I’m currently visualising collecting data for. Some of these cities (not New York yet) provide journey-level data in batches, which is more accurate as it’s not subject to the issues above, but tends to only appear a few months later, and only around five cities have released such data so far.

Places with persistently empty or full docks differ

Here are two maps highlighting docks that are persistently empty (left) or full (right).

nyc_emptyfulldocks

Left map: green = empty <10% of the time, yellow = 10-15%, red = 15-20%, red pins = empty 20%+ of the time. Right map: green = full <2% of the time, yellow = 2-3%, red = 3-4%, red pins = empty 4%+ of the time. Google Maps is being used here. Live version of full map, live version of empty map.

The area near Central Park seems to often end up with empty docking stations, caused perhaps by tourists starting their journeys here, going around Central Park and then downtown. Conversely, Alphabet City, a residential (and not at all touristy) area fairly often has full docking stations – plenty of the bikes for the residents to use to get to work, although not ideal if you are the last one home on a bike.

How the stats were assembled and mapped

As mentioned above, I assembled the stats by looking at the data collected every two minutes, iterating it, and counting changes detected as docking or undocking “events”, while also counting the number of spaces or bikes remaining for the second set of maps.

There are a couple of big flaws to this technique – firstly, if a bike is returned and hired within a single two minute interval (i.e. between measurements) then neither event will be detected, as the total number of bikes in that docking station will have remained constant. This problem mainly affects the busiest docks, and those that see the most variation in incoming/outgoing flows, i.e. near parks and other popular tourist sites. The other issue is that redistribution activities (typically trucks taking bikes from A to B, ideal from full docks to empty docks) are not distinguishable. In large systems, like New York’s, this activity is however a very small proportion of the total activity – maybe less than 5%, and so generally discountable in a rough analysis like this. I detected 1.6 million “events” which equates to 0.8 million journeys which each have a start and end event. The official website is reporting 1.1 million journeys during the same period, suggesting that this technique is able to detect around 64% of journeys.

I’ve used Google Fusion Tables to show the results. Although its “Map” function is somewhat limited, it is dead easy to use – just upload a CSV of results, select the lat/lon columns, create a map, and then set the field to display and which value bins correspond to which pin types. Just a couple of minutes from CSV to interactive map. There are a few other similar efforts out there – which aim to take point-based data and stick it quickly on a map, but Google’s Fusion Tables does the job and is easy to remember.

The data is one month’s worth of journeys – 17 July to 16 August. One note about the popularity map – the data. I am really just scratching at the surface with what can be done with the data. One obvious next step is to break out weekend and weekday activity. There are a few other analysis projects around – this website is analysing the data as it comes in, to an impressive level of detail.

* Any docks added in the last month will probably show as being unpopular at the moment, as it’s an absolute count over the last month, regardless of whether the dock was there or not.

Bikeshare 100

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This is the presentation I gave at the Velo-City 2013 conference last week – I’m uploading it here as quite a few people have asked for it. The PDF contains my whole talk, except for some graphics from a couple of forthcoming papers which haven’t been published yet, and a conceptual image from a paper that we haven’t even started doing the research for yet!

The paper includes an introduction to EUNOIA, which is my main research project. Bikesharing data will form a small but useful part of this two-year EU transport mobility modelling project.

Here is the PDF of the presentation.

At time of presenting last week I was monitoring 85 cities live, since then I’ve added quite a few more (and dropped a couple) so I am now at 98 cities!

The new cities are:

Castellon and Leon were particular headaches, as they don’t have an official live map with location data – so I had to use a combination of third-party location data and manual georeferencing the newer locations! Oxford is also the first system where there are not a fixed number of docks. Implementing this on my map was a bit of a kludge. I’ve assumed that there are always more docks than bikes there, with a minimum 10 docks at each docking station.

Fixed cities are:

I’ve also switched the Mexico City feed to using the citybik.es service.

Dropped cities include Pavia (too small) and Stockholm East (too small). Nantong and Guadalajara have also been put on hold as their feeds have frozen for the last few days.

Cities I’m hoping to add very shortly are:

  • Portugal: Torres Vedras (launches 22 June)
  • France: Clermont-Ferrand (launches 27 June)
  • USA: Chicago (launches 28 June)

So Chicago’s DivvyBikes could well be the 100th system I’m tracking – and my presentation title will be valid!

You can see all the cities I’m tracking on the global view of my Bike Sharing Map – I also took the opportunity at the conference to launch this new, consolidated view.

Velo-City 2013 Review

I was at Velo-City 2013 (a major urban cycling trade conference) in Vienna last Thursday, to present my latest work on the Bike Share Map, EUNOIA’s link to bikesharing, and a CASA research paper update. It was great to be able to attend the conference for free, thanks to winning a ticket in the raffle at last year’s Velo-City in Vancouver.

My paper was presented as the last of four talks, specifically on bikesharing, in the mid-morning session. Despite the session venue being hidden away catacombs deep underneath Vienna City Hall, the room was full with an audience of around 100.

First up was Albert Asséraf of JCDecaux, talking about the history of JCDecaux-built bikesharing systems, staring with Vienna itself 10 years ago:

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The talk focused on Paris which is as large as the other JCDecaux systems put together:

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…although London is closer than the top statistic suggests – London’s, at 8100 bikes, is ~44% of Paris’s 18300 (and London is set to get another ~2000 in the next 6-9 months).

Next up was Hans Dechant, talking about Citybike Wien. The Viennese system is one of Europe’s cheapest – after a 1 EUR one-time verification charge, it’s completely free, as long as journeys are under an hour. It was important to structure charges for longer rentals on a progressively steeper scale, so that bikesharing doesn’t complete directly with daily and longer hires from the established bicycle rental firms:

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The talk also highlighted the intensification (increased density of docking stations) that has taken place in the Viennese system over the last couple of years, moving it to be more in line with other large systems across the world:

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There was also discussion of the automatic maintenance system, where bikes are locked in the system to be picked up for preemptive maintenance, after they have done a certain number of journeys or if they haven’t been used for a long time, even if the users haven’t flagged issues on the bikes.

The third talk, by several people, was on the soon-to-be-launched, but much delayed, Budapest “Bubi Bikes” bikesharing system, now likely to appear in summer 2014 – they are just moving to tender now. The system will be 85% funded by a European Union grant. Trip analysis has been performed to identify the areas of the city most likely to be used for bicycle trips and therefore define the system boundaries – mainly on the east side of the river.

Finally, I was on, and talked about the link between my main project (EUNOIA) and bikesharing:

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I also launched a new global view of my Bike Share Map:

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Finally I touched on some ongoing and potential CASA research into bikesharing, including one possible project we are considering studying the spatial analysis of individual users in London:

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One metric that all three of the previous speakers in the session had mentioned was the average distance between docking stations – it is clearly one which is therefore valued by operators and city/transport authorities, so I included some results of a spatial analysis of bikesharing systems – including docking station density – in the talk.

(I’ll aim to upload most of the slides from my talk in the next few days.)

After the session there were two further main sessions on the day – the first being a round table and the second a “speed date” – in both cases, interested parties gathered around a table in the room and the speaker gave a 10-15 minute talk on their project, then people moved to another table and the talks were repeated. It was a good, lively format – the speed date in particular had 91 tables and 7 “slots” and so I was able to learn about projects as interesting as NextBike‘s technology, the ScratchBikes system (Newcastle) which, via their new Grand Scheme brand, is coming to Headington (Oxford) as OxonBike, a wearable cycle light that changes colour/intensity when turning or braking, and an ambitious project to build a cycleway on the east side of Manhattan – which includes blocking off part of the East River and building a power station underneath a new lake!

As well as the talks there were various opportunities to see trade stands – one thing that struck me was the number of companies now offering bike sharing systems. While my research focus has been on the large, “heavyweight” systems that offer many docks and so present many interesting opportunities for spatial analysis, it is clear that there is a large additional movement towards small-scale, cheap systems which can add this new form of public transport system to an urban area of any size.

One regret from the conference was that there was little presence from cycle companies or operators in the Far East. I would loved to have learnt more about the systems and technologies being used there, but Velo-City 2012 proved to have more coverage from Asia.

I had to leave for my plane as the final event of the day was getting underway – a mass cycle parade using bikeshare bikes and various other borrowed bicycles, around Vienna. Various streets were getting blocked off by the police for the event as I headed towards my bus. The benefit of having a conference organised by the city authorities!

The Top 20 World Bikeshare Cities*

With NYC’s big bikeshare launching on Monday, it’s time to update my list of the world’s biggest systems.

* I’m only including the systems where there is publically accessible live (or near-live) data on the number of bikes available for use. This means that most of the large Chinese systems don’t appear – accurate and up-to-date numbers for many of them are hard to obtain, as they either never had live online maps, or, in several cases, the live running information from them has been recently removed. If they were included, and the data from press releases, news articles and other research sources were accurate, then Chinese systems would make up 17 of the top 20, including the top 4.

The Biggest Bike Sharing Cities with Live Bike Numbers (May 2013)

City Country Data Bikes
May 2011
Bikes
May 2012
Bikes
May 2013
1 Paris** France API 17875 (Dec) 18135+ 18380
2 London Great Britain API 4886 6603+ 8128+
3 New York USA Data* 4683
4 Barcelona Spain API 4993 4482 4303
5 Montreal Canada Data 4309 3943 3954
6 Zhongshan China Data n/a 2491 (Jun) 3919+
7 Brussels Belgium API 1833 (Jun) 2494+ 3723+
8 Lyon France API 3063 (Oct10) 3318+ 3411+
9 Mexico City Mexico Map 1129 1149 3408+
10 Milan Italy Map 1315 1559+ 2681+
11 Changwon South Korea Map n/a 2010 (Jun) 2527+
12 Valencia Spain API 2278 (Dec) 2444+ 2495
13 Nantong China Map n/a n/a 2487 (Jun)
14 Seville Spain API 1941 (Oct10) n/a 2312+
15 Toulouse France API n/a n/a 2091
16 Lille/Tourcoing France API 711 (Sep) 1336+ 1967+
17 Washington DC/Arlington USA Data 918 1402+ 1902+
18 Brisbane Australia 3P 1018 1623+ 1883+
19 Nice France Data 784 (Nov) 1361+ 1400
20 Minneapolis/St Paul USA Data 605 977+ 1392+
21 Taipei City Taiwan Data 336 (Jul) 310 (Mar) 1374+
22 Kaohsiung Taiwan Map n/a 992 (Jul) 1312+

The numbers are coming generally from the monthly recorded max from my Bike Share Map.
* API anticipated appearing soon.
** Not including 400 bikes from a couple of separate systems on the outskirts.

Anecdotal evidence suggests that some “official” information releases overstate the numbers of bikes available – operators may consider bikes that are in docks but broken, in a workshop for repair, or in redistribution vans, to be available for use, whereas I’m counting the maximum recent number of bikes available in docks for people to use at a particular instant. So, for consistency, I’m sticking to the raw statistics that I can see, for the list here.

(I know there’s 22, not 20. I initially missed out Taipei City by mistake, then didn’t have the heart to move out Kaohsiung. Then I found a new datasource for Nantong.)

NYC’s Bikeshare is Almost Here

nycbikes

Not long now – less than a month – until the 1 May launch of New York City’s long-awaited and delayed (the hurricane last year didn’t help) bikeshare system, Citi Bike. Stations are starting to be rolled out.

A pilot test is currently being run with some docking stations and bikes, in the Navy Yard area of Brooklyn. I’ve discovered the live data feed for these stations, and combined it with the “planned stations” data feed, to produce a map of the system as it stands, which I’ve added to my Bike Share Map. The live docking stations are in blue/red colours, depending on how full of bikes they currently are, while the planned stations are in grey – dark grey for Phase 1 (launching in May), and light grey for Phase 2 (later this year). The rollout is happened in two phases due to damage inflicted on some of the warehoused bikes caused by the flooding from Hurricane Sandy late last year. Disasters like that kind of put the occasional complaints about the London’s own system into perspective!

London and New York share a common base design for both the bikes and docking stations, so in theory if you were to fly a Boris Bike to NY, it would fit in a dock’s slot – although I presume the system would then reject it for having an alien ID! Both cities’ docking stations and bikes have had design modifications from the Montreal BIXI original, with London’s docking stations being concreted in to the ground while NY’s docking stations have a solar “tower” for power, and a credit-card shaped slot as well as the normal key slot, for future integration with transport smartcards.

One of the most promising parts of the Citi Bike website is the System Data tab – right there on the front page. This looks like NYC will potentially be joining London, Minneapolis, Washington DC and Boston (and possibly Paris soon) in making anonymous journey data available to anyone who wants to analyse it.

Incidentally I’m delighted to see that the NYC system has an official blog and it looks like it’s not alone, with the largest non-Chinese system in the world, Paris, also having an official blog. Come on London, get blogging!

The London Bike Share Marches North

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It’s not just Wandsworth and Fulham that will be getting Barclays Cycle Hire in the next year or so when Phase 3 goes live – Hackney and Islington will be getting a few too. The iconic “Boris Bikes” will be heading up Mare Street towards central Hackney – although not quite getting there – plus there’ll be various new docking stations in Haggerston, just north of the Regent’s Canal. There will also be a docking station on Islington Green, and a few around the Canal Museum on Calendonian Road. In all, if planning permission is forthcoming, there will be up to 15 new docking stations, all north of the Regent’s Canal. It’s a modest increase – 3% – but the communities affected will doubtless enjoy the new facility. It’s still a long way south from myself though!

I’ve adapted my Bike Share Map to show the proposed locations, above. The potential docking stations appear in green.

It’s great to see that the system is continuing to expand in all directions – but now the central London demand is being sated, it would be nice if Transport for London relaxed their requirement for docking stations to be within 300m of each other. The most successful bike share systems generally have a dense core and a well spaced out periphery, which accommodates commuters, tourists and locals equally well. I would much rather have the system properly penetrating Zone 2 and 3, even if there’s a 1km gap between each docking station. Then it becomes more useful for the utility users who unlike the commuters (going from stations to skyscrapers) and tourists (concentrating on the bigs parks and markets) act as useful re-distributors in their own right by the nature of their diverse journey directions.

Thanks to Loving Dalston for spotting a planning application for the docking station by London Fields. I had a quick trawl through the Hackney and Islington council planning websites to spot the others.

Paris Workshop on Bike Sharing Systems

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I attended a one-day workshop last week, hosted by IFSTTAR’s GERI Animatic research group at École des Ponts ParisTech just east of Paris. The workshop was on Bicycle Sharing Systems, and as I have recently been working with a couple of colleagues, Dr Martin Zaltz-Austwick and Dr James Cheshire, on research relating to bicycle sharing data, and mapping the systems currently live in various cities around the world, I was keen to attend, particular as the agenda was packed with interesting sounding talks.

My rush-hour commute through Paris proved to be slightly more traumatic than planned (I wonder if Parisian visitors find London Underground stations as confusing as I find those on the Paris metro?) but I arrived at the École des Ponts ParisTech in time to hear the workshop organiser introducing the sessions. First up was Pierre Borgnat talking about network analysis of Lyon’s system. I had seen a paper by him on Lyon before, and the popularity and density of Lyon’s system has allowed for a rich and interesting dataset for mining and community detection. The community detection has been done using both spatial and temporal variables. Pierre’s thorough and technical treatment of the data was backed up with some excellent mapping of the data, which you can see above and below.

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Next up was Jon Froehlich. Jon’s talk was underpinned by a discussion of the different data sources and types available in the field. He focussed on temporal cluster analysis of the Barcelona bicycle sharing system (below) – a particularly interesting city for me as, along with London and Zurich, it is a case study for the EU project I have recently started working on, EUNOIA. Barcelona’s bicycle sharing system is not unlike London’s, in terms of its size, shape and usage characteristics – although the general downward slope of the city causes headaches for its operator. Jon gets bonus points for including not only a quote from this blog on his presentation, but Martin’s beautiful routed bike-flow animation for London, and Dr Jo Wood’s more recent bi-directional flow animation, again of London.

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Etienne Côme, from the hosting school, was next on, with an analysis of the biggest system (outside of China) of all – the Vélib in Paris. The Vélib is perhaps the holy grail of academic research in the field as its size, and Paris’s multiple commercial and residential zones, means that community and network analysis is likely to be eye-opening. Similar to Pierre, Etienne outlined eight detected communities, by looking at temporal variations in the origin-matrix between the 1200-odd stations on the Vélib network.

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After lunch, Vincent Aguilera was first on, with a switch away from bicycle sharing systems but showing some techniques that have potential for the field – Vincent looked at using mobile phone network data to detect station dwell times and true journey durations on a section of the RER metro in Paris. He compared this data with Twitter messages with appropriate hashtags (below), and the real-time running supplied by the operator on its website. The availability and structure of the cell-towers on the network allowed a direct comparison to be made – indeed, such data may actually be of better quality than that currently available at the operator’s disposal, allowing more fine-tuned operation and monitoring.

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Neal Lathia was next with a look at London’s system – specially effects caused by the addition of casual (i.e. non-key, non-member) availability in December 2010. The additional option did see some changes in the usages of certain docking stations. The comparison was done by clustering the network’s docking stations by time, before and after the transition, and then seeing which stations changed cluster. One of the main areas of change was in the very heart of London, around the Trafalgar Square area, suggesting a slight shift away from the (still dominating) railway station-based usage patterns.

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Fabio Pinelli’s talk was wide-ranging – it included system design, routing for Dublin’s (over)used system, a look at the reliability of the Vélib fleet.

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Finally, Francis Papon from the hosting school took a step back from the modern electronically managed bicycle sharing systems and mobile/social data sources, and looked at change in uses of urban cycling more generally. His dataset stretched over a hundred years, rather than the typically five-year maximum historical range that bicycle sharing systems have. A key trend is that in the largest French cities studied, including Paris, there is a recent (post-2000) renaissance in urban cycling usage, but this is not matched in many of the country’s smaller cities.

The workshop concluded with a general discussion of the research field to date and its direction. What was particularly interesting was that several bike sharing operators were in attendance, they were fully engaged with the academic research being carried out, asking questions but also revealing some nuggets of information about how the systems are rebalanced, relative costs of operations and why they thought some systems were more successful than others.

Hopefully there will be more such workshops in the future in Europe – with UCL CASA, Cambridge, City University London and LSHTM all involved in the field, maybe there should be one taking place in London next year?

Bike Share Operators and Social Media – User Engagement with Twitter

Many of the bike share operators whose systems I’ve mapped have accounts on Twitter – but do they use them to reply to customers, notify of system changes, or just tweet promotional measures? Have they built up an appropriately large set of followers? Do they tweet often? An active Twitter account is good customer service, one that replies to queries is great customer service! (N.B. Google has translated the Velib conversation above from French.)

There are 24 operators, for which I able to find a relevant Twitter account. The following table shows how they use it. This does of course leave several hundred other operators (many very small) for whom I could not find an account.

City System Name Size (Bikes) Twitter Account Foll-owers Repl-ies Bad News Score
Mexico City Ecobici 1100 @ecobici 21475 Yes Yes *****
Miami Beach DecoBike 540 @DecoBike 10321^ Yes No ****
London Barclays Cycle Hire 6300 @BarclaysCycle 6673 No Yes ***
Washington DC/Arlington Capital Bikeshare 1400 @bikeshare 5374 Yes Yes *****
Denver B-Cycle 400 @Denver_Bcycle 4652 No Yes ***
Minneapolis Nice Ride 1230 @niceridemn 4100 Yes Yes *****
Paris Velib 15700 @Velib_Paris 3094 Yes Yes ****
Boston Hubway 700 @hubway 3741 Yes Yes *****
Montreal BIXI 4300 @BIXImontreal 3214 Yes Yes ****
Toronto BIXI 820 @BIXItoronto 2801 Yes Yes ****
Barcelona Bicing 3800 @bicing 2358 Yes Yes ****
Boulder B-Cycle 120 @Boulder_Bcycle 1541 Yes Yes *****
Lille V’Lille 1550 @transpole_actu 1322^^ Yes Yes ***
Bordeaux VCub 1300 @tbc 1118^^ No No *
Melbourne Melbourne Bike Share 550 @MelbBikeShare 1095 No No ***
Chattanooga Bike Chattanooga 230 @BikeChattanooga 672 Yes No ****
Kansas City B-Cycle 80 @BikeShareKC 608 No Yes ****
Broward County B-Cycle 150 @browardbcycle 498 Yes No ****
Rennes Le Velo Star 630 @levelostar 384 Yes Yes ***
San Antonio B-Cycle 200 @SA_Bcycle 323 No Yes ****
Madison B-Cycle 220 @Madison_Bcycle 319 No Yes ****
Tel-Aviv Tel-o-fun 860 @tel_o_fun 226 Yes Yes ***
Brussels Villo 3100 @Villo_brussels 152 No Yes **
Ottawa Capitale 230 @capitalbixi 145 Yes Yes ***

^ = Account also handles smaller bike share systems in other cities.
^^ = Account also handles other public transport in the city.

Operators get a star for being on Twitter, another for having more followers than bikes on the street, another for replying directly to at least some user queries on Twitter, another for tweeting and least some system issues and other “bad news”, and another for having made at least a couple of tweets in the last 48 hours.

Large (500+ bike) systems with no active official Twitter account that appear on bikes.oobrien.com: Brisbane, Luxembourg City, Lyon, Milan, Nice, Saragossa, Valencia and Vienna. Not including Chinese or South Korea systems as Twitter appears to not be widely adopted in these countries, at least in terms of official transport accounts. Metrics were measured on 21 August 2012.