Category Archives: London

Dockless Bikeshare in London – oBike is Here

London has a new bikeshare system – and it’s appeared by surprise, overnight. oBike is a dockless bikeshare. The company is based in Singapore, where it runs a number of large dockless systems there and in various Chinese cities, Melbourne, Amsterdam and Zurich, it is also likely coming to Washington DC in the USA and to Berlin in Germany, based on some recent job postings.

And now they’ve shipped 29 lorry-loads of nearly 5000 bicycles to London, the number being revealed in a now-deleted tweet by a logistics company:

Just under 1500 have been released so far, initially being “seeded” in groups along the major roads in Tower Hamlets and Hammersmith & Fulham boroughs (400 in each), and more recently in Wandworth, Clapham, Kennington, Lewisham, Waterloo, Harrow* and Enfield*, with “organic” use moving the bikes out as far south as Kingston, and as far east as East Ham (plus possibly in the river near Erith…) There have been several hundred journeys already, with the great majority of bikes having been moved at least once from their initial deployment.

Other players in the space are MoBike (in Manchester), OfoBike (in Cambridge – N.B. website currently down) and YoBike (in Bristol). Another company, GetBike, claimed to have launched in London a few months ago but the bikes, to date, have not appeared. Possibly, they got mired in council discussions. MoBike is also launching a system in Ealing, west London, at the end of the month. All five companies are based in Asia, where mass cycle manufacture is cheap, which had led some cities there ending up with huge heaps of dockless bikeshare bikes, being piled up by desperate city councils trying to keep their pavements clear.

As the bike is the only physical presence on the street, there are no permanent structures for the system and so authorities are not always involved in the process, but have to pick up the pieces and clear the streets – leading some in the dock-based bikeshare establishment to term the systems as rogue bikeshares. The European Cyclists Federation have this week published this timely position paper, where they term the systems slightly more politely as “unlicenced bikeshare” and suggest a potential framework to make the concept work in a European urban context. Whether the operators take notice of course is another matter…

Meanwhile, oBike’s rollout continues. In the map below, red dots with yellow borders show the most recently organically moved bikes (i.e. areas of red/yellow = popular use) while the blue dots with turquoise borders show ones which have not moved since their initial deployment. The other bikes (which someone has moved, but not recently) are shown with purple dots. The map is just a snapshot, and is manually created by myself, so I may have missed some bikes (but I think I’ve got almost all of them):

So far, most of the rollout has been to areas already served by bikeshare – the Boris Bikes (aka Santander Cycles). The real value add for London will be when Zones 3-6 (i.e. non-tourist, non-hipster “real London”) get the bikeshare. After 7 years of the Boris Bikes and no sign of them extending outwards, it’s about time the rest of us got the value of bikeshare too, particularly as our alternative options are more limited.

What is it?

Dockless bikeshare is different from the so-called “third generation” dock-based systems like London’s existing Santander Bicycles or “Boris Bikes”. It does away with docking stations and credit card terminals for charging and storing the bikes and administering the access, instead the bikes themselves have locks which contain a solar panel GPS receiver and SIM card for broadcasting their location, and are controlled by an app on your smartphone. It massively cuts down on the costs of the system because no docking stations are needed. London’s docking stations are very expensive as they have to go through the planning process, and also need to be wired up for power. There are also fewer staff needed – oBike do not employ drivers to redistribute the bikes, and also don’t have an established call-centre. Payments are handled entirely through the app. Maintenance teams are also, I suspect, likely to be minimal on the ground.

The bikes themselves are similar in size to the Boris Bikes, but come with solid rubber tyres (so no punctures). They feel around the same weight. The bikes only have only one gear, set quite low, so you can’t get up much speed. The bikes don’t feel heavier than a Boris Bike. They have the same, chunky “tank” feel to them and feel sturdy – the livery being bright yellow helps with visibility on the streets, which is a bonus.

Trying it Out

I took an oBike out for a spin yesterday afternoon. I noticed a pair parked (orange pins) close to the Facebook office at Euston Square – maybe some Facebookers trying out the latest thing?

On arrival, I was a little surprised to see the bikes were parked on the other side of the road (small blue pin). Still, my own phone’s GPS was saying I was on the other side of a large building (blue dot)…

The process of getting the bike was straightforward – I had already paid the £29 refundable deposit (£49 from August) by entering my card details into the app on my phone, so it was just a case of clicking “Unlock” and the scanning the QR code on the bike’s stem. Around 10 seconds later (with communication through your phone’s Bluetooth or through the SIM card on the lock – I’m not sure) the lock on the bike clicked open and the app’s timer started. Neat! The whole signup process was far more streamlined than with the Boris Bikes, where you have to go to a docking station, use the terminal there, page through tens of screens of information and put in your credit card at least twice. Here, you can be on board in less than a minute, with subsequent hires even quicker.

My bike already had its seat raised to the highest position (or higher still, as there was some brown scuffing there) so no adjustments needed. I headed across Euston Circus and down to the British Museum. Unfortunately, my bike had a distinct squeak every time the back wheel rotated, although squeezing the brake stopped it for a few seconds. The brakes themselves are excellent (perhaps I noticed this particularly as my own bike brakes are poor) and everything seemed OK. The bike appeared in good condition, no rubbish had collected in the basket. The handlebars are very wide, so I couldn’t squeeze through the usual gaps between cars and buses. I didn’t find the handlebars very grippy – they are plastic rather than rubber, and my left hand slipped off at one point (I was juggling a mobile phone at the time). In all, not the fastest cycle but perfect fine for utility riding and definitely still faster than walking or getting the bus.

There were various Boris Bikers around, I must have passed at least 10 in my 15 minute ride, but no other oBikes – yet! At the end of my journey I dropped the bike beside a bike rack beside Euston Square station. It wasn’t immediately obvious how to end the journey – you don’t press anything in the app, instead you pull the lock switch manually back across the back wheel, until you hear a reassuring click. A few seconds later, as long as you have Bluetooth switch on, on your phone, then the app beeps and confirms the journey as complete.

On finishing, the app presents an attractive display showing your start and finish, time, and a “route”, however the route is simple the Google Maps route for bicycles rather than the actual journey taken. The distance also bears no correlation to either the Google Maps “shortest path” route on the map, or the actual distance taken, which is very odd. For the finish location itself, the GPS had once again not given a particularly accurate result, and it looked like I’d cycled the bike straight into the A&E department at University College Hospital. Only 100m or so off again, but not ideal for discovery:

I tried another bike out a bit later. This didn’t have a squeak, however the basket was tilted slightly to one side – not a biggie but still a bit worrying that quirks like this are appearing so soon into the deployment. The bike coped just fine with the rough surface on the River Lea towpath, including over several speedbumps. However, on a return journey, the unlocking process proved to be rather fraught. The QR code was read fine by my phone, but the communication to the lock was not working well, and it kept timing out. Only after around 6 attempts, including moving the bike around. The area we were in had quite poor mobile reception so this may be part of the problem. Still, the few minutes delay to the journey was frustrating. However, once we were moving, the bike itself performed well.

Opportunities

I really like the app, and the payment structure is excellent – 50p flat rate per 30 minutes represents much better value than the £2/day for 30-minute-max journeys on the Boris Bikes. I really didn’t like that, before oBike, it was cheaper to get a bus than a bicycle in London. The reward system is a great idea, it always made sense to incentivise riders to do the tasks of the operator, and the lack of redistribution is another good thing – I always thought it was a huge waste of time redistributing bicycles to one place, only to redistribute them back later.

The fact that, rather being constrained to docks, the allowed operating area is the whole of London, is great. Already, one bike has ended up at Heathrow in the far west of London:

The lack of docks mean that the users set the area of coverage. Finally, Hackney and Haringey, Lewisham and Rotherhithe, have the bikeshare that I am sure would always have been popular there.

I do also like the name – oBike (five letters, two syllables) rolls off the tongue a lot more easily than Barclays Cycle Hire or Santander Cycles.

Challenges

I’m not totally convinced that oBike will survive long-term – despite assertions to the contrary by some operators of these dockless bikeshares, the bikes will need maintenance due to the rough weather, roads and people in London. Whether they get any will be interesting to see. The bikes I tried out have only been on the streets for four days, and for the first one I tried have picked up a loud squeak so quickly, and the second one to have a wonky basket, is not great. The bikes are also a little too small for the British frame – having said that I am 6′ and I got around OK on one, but with the seat-post extended to its absolute maximum. There have been some cases of the seat-posts easily coming right off when extended further.

Also – some people will inevitably be hard on the bikes. They are not that indestructible, and some people will see them as a cheap way of getting a bike. Some will end up in the Thames. Councils will end up confiscating some, as Hammersmith & Fulham has already threatened to do. Not getting the council involved is brave – they may have looked at the Cambridge example where the council insisted that another operator reduce its launch from 500 bikes to 20 – there are obvious advantages with not having to deal with 33 separate councils in London (+ the City + TfL and the GLA) and just sticking the bikes out there – but oBike could have made life easier for themselves by distributing them more discretely, to avoid the ire of grumpy councils and pedestrians – placing one or two bikes together, at the most, ad on side roads rather than main roads, beside existing cycle parking racks, rather than obstructing pavements, and focusing on Zones 3-6 first (even if that results in a slower initial takeup). And a commitment to maintenance or organised disposal would also be good – at the moment no one knows what will happen to the bikes after they start to wear out. The scenes of “bicycle graveyards” and huge heaps of brightly coloured bicycles, in the cities of the far east that are full of dockless bikeshares, are worrying.

I hope that oBike is a success, and the bicycles survive grumpy councils, the kids who just want a free bike, and the weather. If it provides an incentive to give the Boris Bikes a kick up the backside (50p per 30 minutes flat rate and all-London low density coverage please!) then that on its own would be a result. Providing a bikeshare out into London’s Zone 3 and further is a real winner for shared mobility options outside London’s already well connected central core.

* These have since been removed, I understand, following discussions with the councils there. The company however did not switch off the GPS trackers on the removed bikes, and so have revealed the location of their depot, at Rainham on the very edge of east London:

Map background Copyright HERE Maps. Top photo Copyright JC, bottom photo Copyright SR.

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High quality lithographic prints of London data, designed by Oliver O'Brien

Evolution of London’s Rush Hour Traffic Mix

My latest London data visualisation crunches an interesting dataset from the Department of Transport. The data is available across England, although I’ve chosen London in particular because of its more interesting (i.e. not just car dominated) traffic mix. I’ve also focused on just the data for 8am to 9am, to examine the height of the morning rush hour, when the roads are most heavily used. 15 years worth of data is included – although many recording stations don’t have data for each of those years. You can choose up to three modes of transport at once, with the three showing as three circles of different colours (red, yellow and blue) superimposed on each other. The size of each circle is proportional to the flow.

It’s not strictly a new visualisation, rather, it’s an updated version of an older one which had data from just one year, using “smoothed” counts. But it turns out that the raw counts, while by their nature more “noisy”, cover a great many more years and are split by hours of the day. I’ve also filtered out counting stations which haven’t had measurements made in the last few years.

Note also the graph colours and map colours don’t line up – unfortunately the Google Material API, that I am using for the charting, does not yet allow changing of colours.

An alternate mode for the map, using the second line of options, allows you to quantify the change between two years, for a single selected type of transport. Green circles show an increase between the first and second year, with purple indicating decreases.

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High quality lithographic prints of London data, designed by Oliver O'Brien

Lives on the Line v2: Estimated Life Expectancy by Small Areas

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I’ve produced an updated version of a graphic that my colleague Dr James Cheshire created a few years ago, showing how the estimated life expectancy at birth varies throughout the capital, using a geographical tube map to illustrate sometimes dramatic change in a short distance.

You can see an interactive version on my tube data visualisation platform. Click a line colour in the key on the bottom right, to show just that line. For example, here’s the Central line in west London.

The data source is this ONS report from 2015 which reports averages by MSOA (typical population 8000) for 2009-2013. I’ve averaged the male and female estimates, and included all MSOAs which touch or are within a 200m radius buffer surrounding the centroid of each tube, DLR and London Overground station and London Tram stops. I’ve also included Crossrail which opens fully in 2019. The technique is similar to James’s, he wrote up how he did it in this blogpost. I used QGIS to perform the spatial analysis. The file with my calculated numbers by station is here and I’m planning on placing the updated code on GitHub soon.

livesontheline_alllondon

My version uses different aggregation units (MSOAs) to James’s original (which used wards). As such, due to differing wards and MSOAs being included within each station’s buffer area, you cannot directly compare the numbers between the two graphics. An addition is that I can include stations beyond the London boundary, as James’s original dataset was a special dataset covering the GLA area only, while my dataset covers the whole of England. The advantage of utilising my data-driven platform means that I can easily update the numbers, as and when new estimates are published by the ONS.

Estimating life expectancies at birth for small areas, such as MSOAs, is a tricky business and highly susceptible to change, particularly due London’s high rates of internal migration and environmental change. Nevertheless it provides a good snapshot of a divided city.

View the interactive version.

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Data: ONS. Code: Oliver O’Brien. Background mapping: HERE Maps.

Smart Mobility Meeting in Mexico City

Below is a presentation that combined my talks last Thursday and Friday at the Smart Mobility forums in central Mexico City, organised by ITDP Mexico and funded by the Foreign and Commonwealth Office’s Prosperity Fund (respresented by the British Embassy in Mexico). The Thursday presentation focused on the third-party app ecosystem that exists around bikesharing in London and elsewhere, while the Friday presentation included more examples of private sector innovation using open data:

My week in Mexico City also included a visit to CIC at IPN (the computational research centre city’s main polytechnic) where I was introduced to a product building visualisations of ECO-BICI data to help create more effective strategies for redistribution. I also visited LabCDMX, a research group and ideas hub to study Mexico City that has been created by the city government, to give a couple of talks in their rooftop on visualising London transit and a summary of web mapping technologies. The organisers also squeezed in a couple of short TV interviews, including Milenio Noticias (23 minutes in). The week ended with a tour of the ECO-BICI operations, repair, management and redistribution warehouse, located centrally and a hive of activity. This included a look at their big-screen redistribution map and vehicle routing system.

Some of the companies and products I cited included CityBikes, Cycle Hire Widget, TransitScreen, ITO World, Shoothill, Waze, Strava Metro and CityMapper. I also showed some academic work from myself, James Cheshire and Steve James Gray in UCL GSAC and UCL CASA respectively, an article in The Guardian by Charles Arthur, an artwork by Keiichi Matsudaa and a book by James Cheshire and Oliver Uberti. I also mentioned WhatDoTheyKnow and heavily featured the open data from Transport for London.

I also featured some work of my own, including CDRC Maps, TubeHeartbeat, London Panopticon, Tube Stats Map, CityDashboard, Bike Share Map and London Cycling Census map.

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Big Data Here: The Code

So Big Data Here, a little pop-up exhibition of hyperlocal data, has just closed, having run continuously from Tuesday evening to this morning, as part of Big Data Week. We had many people peering through the windows of the characterful North Lodge building beside UCL’s main entrance on Gower Street, particularly during the evening rush hour, when the main projection was obvious through the windows in the dark, and some interested visitors were also able to come inside the room itself and take a closer look during our open sessions on Wednesday, Thursday and Friday afternoons.

Thanks to the Centre for Advanced Spatial Analysis (CASA) for loaning the special floor-mounted projector and the iPad Wall, the Consumer Data Research Centre (CDRC) for arranging for the exhibition with UCL Events, Steven Gray for helping with the configuration and setup of the iPad Wall, Bala Soundararaj for creating visuals of footfall data for 4 of the 12 iPad Wall panels, Jeff for logistics help, Navta for publicity and Wen, Tian, Roberto, Bala and Sarah for helping with the open sessions and logistics.

The exhibition website is here.

I created three custom local data visualisations for the big screen that was the main exhibit in the pop-up. Each of these was shown for around 24 hours, but you can relive the experience on the comfort of your own computer:

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1. Arrival Board

View / Code

This was shown from Tuesday until Wednesday evening, and consisted of a live souped-up “countdown” board for the bus stop outside, alongside one for Euston Square tube station just up the road. Both bus stops and tube stations in London have predicted arrival information supplied by TfL through a “push” API. My code was based on a nice bit of sample code from GitHub, created by one of TfL’s developers. You can see the Arrival Board here or Download the code on Github. This is a slightly enhanced version that includes additional information (e.g. bus registration numbers) that I had to hide due to space constraints, during the exhibition.

Customisation: Note that you need to specify a Naptan ID on the URL to show your bus stop or tube station of choice. To find it out, go here, click “Buses” or “Tube…”, then select your route/line, then the stop/station. Once you are viewing the individual stop page, note the Naptan ID forms part of the URL – copy it and paste it into the Arrival Board URL. For example, the Naptan ID for this page is 940GZZLUBSC, so your Arrival Baord URL needs to be this.

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2. Traffic Cameras

View / Code

This was shown from Wednesday evening until Friday morning, and consisted of a looping video feed from the TfL traffic camera positioned right outside the North Lodge. The feed is a 10 second loop and is updated every five minutes. The exhibition version then had 12 other feeds, surrounding the main one and representing the nearest camera in each direction. The code is a slightly modified version of the London Panopticon which you can also get the code for on Github.

Customisation: You can specify a custom location by adding ?lat=X&lon=Y to the URL, using decimal coordinates – find these out from OpenStreetMap. (N.B. TfL has recently changed the way it makes available the list of traffic cameras, so the list used by London Panopticon may not be completely up-to-date.)

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3. Census Numbers

View / Code

Finally, the screen showed randomly chosen statistical numbers, for the local Bloomsbury ward that UCL is in, from the 2011 Census. Again, you can see it in action here (wait 10 seconds for each change, or refresh), and download the code from GitHub.

Customisation: This one needs a file for each area it is used in and unfortunately I have, for now, only produced one for Bloomsbury. The data originally came, via the NOMIS download service, from the Office for National Statistics and is Crown Copyright.

bdh_traffic3

Big Data Here

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The Consumer Data Research Centre (CDRC) at UCL is organising a short pop-up exhibition on hyperlocal data: Big Data Here. The exhibition is taking place in North Lodge, the small building right beside UCL’s main entrance. The exhibition materials are supplied by the Centre for Advanced Spatial Analysis (CASA).

Inside, a big projection shows local digital information. What the screen shows will change daily between now and Friday, when the exhibition closes. Today it is showing a live to-the-second feed of bus arrivals at the bus stop outside the North Lodge, and tube train arrivals at Euston Square station just up the road. Watch the buses zip by as they flash up “Due” in big letters on the feed. Both of these are powered by Transport for London’s Unified Push API, and we are planning on publishing the visualisation online next week. Tomorrow will be showing a different local data feed, and then a final one on Friday.

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Opposite the projection is the iPad Wall. This was created by CASA a few years back by mounting a bank of iPads to a solid panel (above photo shows them in test mode) and allowing remote configuration and display. The wall has been adapted to show a number of metrics across its 12 panels. Four of these showcase footfall data collected by one of our data partners, and being used currently in CDRC Ph.D. research. The other panels show a mixture of air quality/pollutant measures, tube train numbers and trends, and traffic camera videos.

We hope that passersby will enjoy the exhibition visuals and use them to connect the real world with the digital space, a transposition of a digital data view onto the physical street space outside.

The exhibition runs 24 hours a day until Friday evening, with the doors open from noon until 3:30pm each day. The rest of the time, the visualisations will be visible through the North Lodge’s four windows. The exhibition is best viewed at night, where the data shines out of the window, spilling out onto the pavement and public space beyond:

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Big Data Here is taking place during Big Data Week 2016. Visit the exhibition website or just pop by UCL before Friday evening.

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Busiest Tube Station Times

chesham_max

Here are the busiest Tube station quarter-hour periods, based on the Transport for London 2015 RODS data (modelled, based on typical autumn weekday), used in Tube Heartbeat, adding together entries, exits and interchange stats and excluding Kensington Olympia which does not have a frequent Tube service.

The main pattern shows that stations further out (map) from London’s main work areas (The West End, the City and Canary Wharf) have an earlier morning peak (or later evening peak), due to the journey taking longer and the tendency for many people to arrive at their work-end station at about the same time – just before 9am. A secondary effect is that stations which just act as simple commuter home and work portals, we would expect the usage to peak in the morning rush hour, rather than than evening one, as the morning rush hour is shorter and so the simple commuter flow is more concentrated. Therefore, stations which show a peak in the evening are often due to a combination of this simple commuter flow and an evening “going out” destination.

Station Peaks by Time of Day

7:15am-7:30am: Chesham [Zone 9]

7:30am-7:45am: Chalfont & Latimer [8], Epping [6]

7:45am-8:00am: Amersham [9], Chorleywood [7], Debden [6], Elm Park [6], Hillingdon [6], Hornchurch [6], Theydon Bois [6], Cockfosters [5], Pinner [5], South Ruislip [5], Stanmore [5], Mill Hill East [4], Chigwell [4], Grange Hill [4], Perivale [4],Kew Gardens [3/4], Wimbledon Park [3], Holland Park [2]

8:00am-8:15am: Alperton, Arnos Grove, Balham, Barking, Barkingside, Becontree, Buckhurst Hill, Canons Park, Chiswick Park, Clapham South, Colindale, Colliers Wood, Croxley, Dagenham East, Dagenham Heathway, Eastcote, East Putney, Edgware, Fairlop, Finchley Central, Gants Hill, Hainault, Harlesden, Harrow-on-the-Hill, Hatton Cross, High Barnet, Hounslow Central, Hounslow East, Hounslow West, Ickenham, Kenton, Kingsbury, Loughton, Moor Park, Morden, Neasden, Newbury Park, Northfields, North Harrow, Northolt, Northwick Park, Northwood, Northwood Hills, Oakwood, Osterley, Parsons Green, Preston Road, Ravenscourt Park, Rayners Lane, Redbridge, Rickmansworth, Roding Valley, Ruislip, Ruislip Gardens, Ruislip Manor, Seven Sisters, Snaresbrook, South Ealing, Southfields, Southgate, South Harrow, South Kenton, South Wimbledon, Stamford Brook, Sudbury Hill, Sudbury Town, Totteridge & Whetstone, Turnham Green, Upminster Bridge, Upney, Wanstead, Watford, West Acton, West Harrow, West Ruislip, Wimbledon, Woodford, Woodside Park

8:15am-8:30am: Acton Town, Archway, Arsenal, Blackhorse Road, Boston Manor, Bounds Green, Bow Road, Brent Cross, Brixton, Bromley-by-Bow, Burnt Oak, Canada Water, Canning Town, Dollis Hill, Ealing Broadway, Ealing Common, East Acton, East Finchley, Finchley Road, Finsbury Park, Fulham Broadway, Golders Green, Goldhawk Road, Hammersmith (H&C), Harrow & Wealdstone, Hendon Central, Highgate, Kensal Green, Kilburn, Kilburn Park, Leytonstone, Maida Vale, Manor House, North Acton, North Wembley, Park Royal, Plaistow, Putney Bridge, Queen’s Park, Shepherd’s Bush Market, St. John’s Wood, South Woodford, Swiss Cottage, Tooting Bec, Tooting Broadway, Tottenham Hale, Tufnell Park, Upton Park, Walthamstow Central, Warwick Avenue, Wembley Park, West Brompton, West Finchley, West Hampstead, Willesden Green, Wood Green

8:30am-8:45am: Baker Street, Bank/Monument, Barons Court, Belsize Park, Bermondsey, Caledonian Road, Canary Wharf, Chalk Farm, Earl’s Court, Edgware Road, Elephant & Castle, Euston, Hammersmith, Hampstead, Highbury & Islington, Holloway Road, Kennington, Kentish Town, Ladbroke Grove, Lancaster Gate, London Bridge, Marylebone, Mile End, Moorgate, Notting Hill Gate, Oval, Paddington, Pimlico, Richmond, Royal Oak, Stepney Green, Stockwell, Uxbridge, Vauxhall, Victoria, Westbourne Park, West Kensington, Westminster, Whitechapel

8:45am-9:00am: Barbican, Aldgate East, Blackfriars, Borough, Cannon Street, Chancery Lane, Edgware Road (Bakerloo), Euston Square, Farringdon, Great Portland Street, Latimer Road, Mansion House, Old Street, Regent’s Park, Southwark, St. James’s Park, St. Paul’s, Warren Street

3:30pm-3:45pm: North Ealing

5:00pm-5:15pm: Heathrow Terminal 5

5:15pm-5:30pm: Willesden Junction

5:30pm-5:45pm: Aldgate, Russell Square, South Kensington, West Ham, Heathrow Terminals 1 2 3, Heathrow Terminal 4

5:45pm-6:00pm: Bond Street, Embankment, Goodge Street, Green Park, Gunnersbury, Hanger Lane, Wood Lane, Holborn, King’s Cross St. Pancras, Knightsbridge, Lambeth North, Liverpool Street, Mornington Crescent, North Greenwich, Oxford Circus, Stonebridge Park, Charing Cross, Stratford, Temple, Tower Hill, Turnpike Lane, Upminster, Waterloo, White City

6:00pm-6:15pm: Angel, Camden Town, Covent Garden, East Ham, Gloucester Road, Greenford, High Street Kensington, Hyde Park Corner, Leicester Square, Leyton, Marble Arch, Piccadilly Circus, Queensway, Shepherd’s Bush, Sloane Square, Tottenham Court Road

6:15pm-6:30pm: Bayswater [1], Bethnal Green [2], Clapham Common [2], Clapham North [2], Queensbury [4], Wembley Central [4]

You can explore graphs of the flows, in detail, at Tube Heartbeat – just choose the station of your choice on the drop-down on the top right, or click on it on the map.

Six Rush Hours?

Interestingly, if you look at the flows between stations, you can actually see SIX rush hours each weekday (you can see five of them below by looking across these sample segment graphs):

fiverushhours

These are:

  • A early morning peak, 7-8am. This is distinct from the main morning peak, and can be seen certain segments in east London, particularly on the District line near Plaistow, where the two morning peaks are an hour apart, with a noticeable dip in flow between the two. This may reflect the workforce for some traditional industries with 8am-4pm historical or shift-based working hours.
  • The main morning rush hour that almost all stations and line segments see – 7:30am-9am. Some of the more outlying stations (Zones 5-9) see their peak for this rush hour earlier than 8am, as it takes a while to get into the centre of London. You can see this is not the 7-8am peak above, by “tracing” the ripple through the network towards central London.
  • School home-time at roughly 3-4pm. Mainly affects some smaller, outer London stations, particularly in the north-west, for example Moor Park.
  • A corresponding 4-5pm peak for shift workers who started at 8am. Only a few links show this, such as Wembley Central in north-west London. The evening rush hours are less “compressed” than the morning ones so it is generally harder to distinguish between this one and the next one.
  • The main evening rush hour, 5-7pm.
  • Theatreland end-of-show rush hour, 10-11pm. Noticeable around Leicester Square, Covent Garden and Holborn. Some other areas, with established night-time economies, may also see a slight peak around this time.

You can also see 3+ rush hours in some of the stations, such as Wembley Central, which shows all six:

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Tube Heartbeat

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Tube Heartbeat is a interactive map that I recently built as part of a commission by HERE, using the HERE JavaScript API. It visualises a fascinating dataset that TfL makes available sporadically – the RODS (Rolling Origin Destination Survey) – which reveals the movements of people on the London Underground network in amazing detail.

The data includes, in fifteen-minute intervals throughout a weekday, the volume of tube passengers moving between every adjacent pair of stations on the entire tube network – 762 links across the 11 lines. It also includes numbers entering, exiting and transferring within each of the 268* tube stations, again at a 15 minute interval from 5am in the morning, right through to 2am. It has an origin/destination matrix too, again at fine-grained time intervals. The data is modelled, based on samples of how and where passengers are travelling, during a specimen week in the autumn – a period not affected either by summer holidays or Christmas shopping. The size of the sample, and the careful processing applied, means that we can be confident that the data is an accurate representation of how the system is used. The data is published every few years – as well as the most recent dataset, I have included an older one from 2012, to allow for an easy comparison.

As well as the animation of the data, showing the heartbeat of London as the the lines pulse with passengers squeezing along them, I’ve including graphs for each station and each link. These show all sorts of interesting stats. For example, Leicester Square has a huge evening peak, when the theatre-goers head for home:

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Or Croxley, in suburban north-west London, with a very curious set of peaks, possibly relating to the condensed school day:

croxley

Walthamstow (along with some other east London stations) has two morning rush-hours with a slight lull between them:

walthamstow

Check the later panels in the Story Map, the intro which appears when first viewing Tube Heartbeat, for more examples of local quirks.

This is my first interactive web map produced using the HERE JavaScript API – in the past, I have extensively used the OpenLayers, as well as, a long while back, Google Maps API. The API was quick to pick up, thanks to good examples and documentation, and while it isn’t quite as full-featured as OpenLayers in terms of the cartography, it does include a number of extra features, such as being quickly able to implement direction arrows along lines, and access to a wide variety of HERE map image tiles. I’m using two of these – a subdued gray/green background map for the daytime, and an equivalent darker one for the evening data. You’ll see the map transition between the two in the early evening, when you “play” the animation or scrub the slider forwards.

Additionally, I’ve overlayed a translucent light grey rectangle across the map, which acts to further diffuse the background map and highlight the tube data on top. The “killer” feature of HERE JavaScript API, for me, is that it’s super fast – much faster than OpenLayers for displaying complex vector-based data on a map, on both computer and smartphone. Being part of the HERE infrastructure makes access to the wide range of HERE map tiles, with their distinctive design, easy, and gives the maps a distinctive look. I have previously used HERE mapping for some cities in the Bike Share Map (& another example), initially where the OpenStreetMap base data was low in detail for certain cities, but now for all new cities I “onboard” to the map. The attractive cartography works well at providing context for the bikeshare station data there, and the tube flow data here.

There is some further information about the project on the HERE 360 blog, and I am looking to publish a more deatiled blogpost soon about some of the technical aspects of putting together Tube Heartbeat.

Stats

Number of stations Number of lines Number of line links between stations
268* 11 762

Highest flows of people in 15 minutes, for the four peaks:

Between stations (all are on Central line)
Morning 8208 0830-0845 Bethnal Green to Liverpool Street
Lunchtime 2570 1230-1245 Chancery Lane to Holborn
Afternoon 7166 1745-1800 Bank/Monument to Liverpool Street
Evening 2365 2230-2245 St Paul’s to Bank/Monument
Station entries
Morning 7715 0830-0845 Waterloo
Lunchtime 1798 1130-1145 Victoria
Afternoon 5825 1730-1745 Bank/Monument
Evening 2095 1015-1030 Leicester Square
Station interchanges
Morning 5881 0830-0845 Oxford Circus
Lunchtime 2060 1330-1345 Oxford Circus
Afternoon 5043 1745-1800 Oxford Circus
Evening 1109** 2215-2230 Green Park
Station exits
Morning 6923 0845-0900 Bank/Monument
Lunchtime 2357 1145-1200 Oxford Circus
Afternoon 7013 1745-1800 Waterloo
Evening 1203 1015-1030 Waterloo

* Bank/Monument treated as one station, as are the two Paddington stations.
** Other stations have higher flows at this time but as a decline from previous peak.

I’m hoping to also, as time permits, extend Tube Heartbeat to other cities which make similar datasets available. At the time of writing, I have found no other city urban transport authority that publishes data quite as detailed as London does, but San Francisco’s BART system is publishes origin/destination data on an hourly basis, there is turnstyle entry/exit data from New York’s MET subway, although only at a four-hour granularity, and Washington DC’s metro also publishes a range of usage data. I’ve not found an equivalent dataset elsewhere in Europe, or in Asia, if you know of one please do let me know below.

tubeheartbeat2

The data represented in Tube Heartbeat is Crown copyright & database right, Transport for London 2016. Background mapping imagery is copyright HERE.

London Panopticon

Panopticon Animation

The London Panopticon utilises the traffic camera feed from the TfL API, which recently (announcement here) added ~6-second-long video clips from the traffic cameras on TfL “red route” main roads, to show the current state of traffic near you. The site loads the latest videos from the nearest camera in each compass direction to you. The images are nearly-live – generally they are up-to-date to within 10-15 minutes. If the camera is “in use” (e.g. being panned/zoomed or otherwise operated by an official to temporarily reprogramme the traffic lights, see an incident etc) then it will blank out. The site is basically just JavaScript, when you view it, your browser is loading the videos directly from TfL’s Amazon cloud-based repository.

The Panopticon continuously loops the video clips, and updates with the latest feed from the cameras every two minutes, the same frequency as the underlying source. If you are not in London or not sharing your location, it will default to Trafalgar Square. I’ve added a special “Blackfriars” one which is where the under-construction Cycle Superhighway North/South and East/West routes converge – during rush hour you can already see bursts of cyclists using the new lanes.

Try it at vis.oobrien.com/panopticon and note that it only works on desktop web browsers (I’ve tested it on Chrome, Firefox and Safari). It didn’t work on Internet Explorer “Edge” when I tested it on a PC. It also does not work on Chrome on Android and by extension probably mobile in general. It possibly uses a lot of bandwidth, so this is perhaps just as well.

I’ve named it after the Panopticon, a concept postulated by Jeremy Bentham, co-founder of University College London, where I work, in the 1800s for easy management of prisons. The Panopticon encourages good behavior, because you can’t see the watcher, so you never know if you are being watched. Kind of like the traffic cameras.

The concept evolved from a special “cameras” version (no longer working) of the London Periodic Table, which was itself a follow-on from CityDashboard, both of which I created at CASA. The source is on GitHub.

londonpanopticon

p.s. If you made it this far, you might be interested in a hidden feature, where you can specify a custom location. Just add ?lat=X&lon=Y to the URL, where the X/Y is your desired latitude/longitude respectively, in decimal coordinates. Example: http://vis.oobrien.com/panopticon/?lat=51.5&lon=0.

London’s Bikeshare Needs A Redistribution of Stations

bikes_journey_day

Here’s an interesting graph, which combines data on total journeys per day on London’s bicycle sharing system (currently called “Santander Cycles”) from the London Data Store, with counts of available bicycles per day to hire, from my own research database. The system launched in summer 2010 and I started tracking the numbers almost from the start.

You can see the two big expansions of the system as jumps in the numbers of available bikes – to all of Tower Hamlets in early 2012, and to Putney and Fulham in late 2013. Since then, the system has somewhat stagnated in terms of its area of availability, although encouragingly at least the numbers of available bikes has remained constant at around 9500, suggesting that at least the operator is on top of being able to maintain and repair the bikes (or regularly source new ones). Some of the individual bikes have had 4000 trips on them. There is a small expansion due in the Olympic Park in spring 2016, but the 8 new docking stations represents only a 1% increase in the number of docking stations across the system, so I doubt it will have a significant impact on the numbers of available bikes for use.

There is a general downward trend in the numbers of uses of each bike per day, since the halycon Olympic days of Summer 2012, over and above the normal seasonal variation, which concerns me. The one-year moving average recently dipped below 3 uses of each bike per day, this summer, and I am not confident it will pick up any time soon. (The occasional spikes in uses/bike, by the way, generally correspond to sunny summer bank holidays, tube strikes and Christmas Day).

To rejuvenate the system and draw in more users, rather than relying on the established commuter and tourist flows which likely dominate the current usage, I am convinced that the system needs to expand – not necessarily in terms of the number of bikes or docking stations, but in its footprint. I think the system would be much improved by dropping the constraining rule on density (which approximates to always having one docking station every 300m) and instead redistributing some of the poorly performing docking stations themselves further out. It’s crazy that, five years on, there are no docking stations in central Hackney, Highbury, or Brixton, three areas with an established cycling culture and easily cycle-able into the centre of London. Conversely, Putney and Tower Hamlets simply don’t need the high density of docking stations that they currently have, except in specific areas (such as around the train/tube stations in Putney, and Canary Wharf).

Ideally we would have a good density of docking stations throughout cycleable London but, as docking stations (and bikes) are very expensive, I would suggest that TfL instead adopts the model used in Bordeaux (below). Here, the city retains a high-dense core serving tourists, commuters and other centrally-based workers, but adopts a much lower density in the suburbs, so that, while tourists can still “run into” docking stations they don’t know about in the centre thanks to the high density, local users can benefit from the facility in their neighbourhood too, even if it requires a little longer walk to get to it.

bikes_bordeaux

Technical note: Before November 2011, the London numbers included bicycles that were in a docking station but not available to hire (i.e. marked as broken). This exaggerates the number of available bikes (and correspondingly reduces the number of hires/bike/day from the true value) in this period by a small amount – typically around 3-5%, an effect I am not considering significant for this analysis.