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Commonly called ‘Boris bikes’ after London mayor Boris Johnson, they’re not just a popular way of getting around. Along with similar schemes around the world, it’s become fertile territory for scientists who are using it to reveal new insights into commuting, tourism, and even our health.
London is just one of 763 cities around the world with cycle sharing schemes, as shown on the Bike-sharing World Map maintained by Russell Meddin. Many of them are relatively new. Yet big schemes in urban areas date back to 1965, when white cycles were left around Amsterdam for anyone to use free of charge.
Later schemes introduced cash deposits, with bikes fastened by locks like those on supermarket trolleys. Yet these early schemes foundered, with bikes often being stolen or vandalised. It would take a picturesque city in France to kick-start the cycle-sharing revolution.
In 2005, distinctive silver-and-red bikes and a handful of technological innovations landed in Lyon. Just as Apple’s iPhone was greater than the sum of its individual parts, the Vélo’v scheme cleverly combined technologies like card payments, ‘docking stations’ connected to the internet and bikes with built-in chips.
Velo’v was a huge success, and its key features were copied by later schemes. Card payments and membership schemes deterred thieves and were also more convenient for users, as were electronic docking stations. By using a website like London’s ‘Find a docking station’, you can easily find a location with bikes available for hire.
Not all modern cycle sharing schemes have docking stations, though. Users of ‘Call a Bike’ in German cities like Berlin use a smartphone app to hire a cycle, which they can leave locked up anywhere. But it’s the schemes with fixed docking stations that are providing the most useful data, because the limits on the number of possible journeys makes the data possible to analyse.
A single journey from docking station A to docking station B provides the start and end locations, the day of the week, the exact times the user set off and arrived, and the direction of travel. Because the approximate distance is known, you can even make a rough guess at the user’s average speed.
Barclays Cycle Hire publishes some of these statistics in quarterly reports. In the summer of 2013, for example, three of the top four most frequently made journeys by members were from Waterloo Station – one to Holborn and the rest to St Paul’s. Both destinations are convenient for the City, London’s financial heart.
Journeys made by users who make one-off payments, on the other hand, tell a different story. The most frequent journey began at Hyde Park corner and ended at... Hyde Park Corner. In fact, nine of the top 10 journeys started or ended in Hyde Park –a safe and scenic place for tourists to enjoy a ride in the summer sun.
Interesting though the raw data is, it’s far more compelling when viewed on a map. Jo Wood, Professor of Visual Analytics at City University, did just that by animating Barclays Cycle Hire’s first 16 million journeys. As he showed in a TEDx talk, the map of two years’ worth of journeys is a jumbled mess that “looks like brain activity”.
But as Wood stripped away all but the most common journeys, patterns emerged. Hubs formed around the tourists’ favourites routes - Hyde Park, Regent’s Park and the Isle of Dogs - while routes north from Waterloo and south from King’s Cross railway stations reveal commuters on their journeys in and out of the capital.
The data helps in the day to day running of Barclays Cycle Hire, explains its Head of Operations, James Mead. “By understanding the time and the destination of users, we can make sure we’ve got staff where we need them - which impacts on the hiring and retrieving of bikes, but also on the people who do cleaning or docking station maintenance.
Looking at the data also helps Barclays Cycle Hire work out how best to expand. “We’re in the midst of working with the London boroughs to expand docking stations where we need extra capacity, or put ones in nearby if there’s not enough space at an existing docking station,” reveals Mead.
But perhaps the most important use of data is for solving the “rebalancing problem”. Rebalancing requires transporting bikes in vans from one docking station to another. Users need bikes to be available, but they cannot return them if stations are full. Some routes are bound to be more popular than others, such as those going downhill. In Barcelona, for example, bikes tend to accumulate down at the beach.
Keeping bikes where they’re needed is a never-ending task but science provides a helping hand, explains Mead. “The operations centre uses a system that schedules vans for trips based on how we anticipate a station will get full or empty. So when we get new data, we use it to update the algorithms that are used in scheduling. We still have people doing manual overrides every day, though, because it doesn’t always work out exactly as you plan it.”
The algorithms are attempts at solving an extremely complex mathematical problem. To get an idea of just how complex, take a look at Instances and formulations for the bike sharing rebalancing problem by the University of Modena’s Mauro Dell'Amico. [Warning: you’ll need a university degree in maths to understand it.] It’s not even possible to come up with an exact solution to the rebalancing problem, although scientists like Dell'Amico are working on making the algorithms more efficient.
While data from journeys can help transport planners in the short-term, it has long-term implications when combined with details about the users themselves. In London, for example, a greater proportion of women make the less popular journeys – these are often routes associated with parks. So if you want to encourage more women to make the commuter runs, for example, it might take changes to road layouts on busy streets.
Insights gleaned from other schemes were compiled in January 2014 by UCL’s Oliver O’Brien and colleagues in the Journal of Transport Geography. Some of the more interesting come from Lyon’s Vélo’v, the granddaddy of high-tech cycling schemes. In Lyon, the average speed is higher before 9am, as commuters hurry into work. But speeds are also fastest on a Wednesday. Why? It could be because it’s the most popular stay-at-home day for working mothers, so Wednesday leaves a greater proportion of men pedalling around Lyon.
When journey data is combined with other statistics, the results can be even more illuminating. Recent research published in The BMJ into the health impact of the London cycling scheme found that the benefits of physical activity outweighed not only the risk of injury but also the exposure to the pollutant PM2.5, the biggest airborne threat to health. The scientists combined anonymised data on users with sets of health statistics and a pollution map of the capital.
The scientists went on to carry out a second analysis, using injury rates for London cyclists as a whole - not just cycle hire users. This time, they found much smaller benefits for women and young people aged 15-29 years old. The discrepancy is partly because cycle hire users are younger than the population, and it’s older people who benefit most from exercise. It’s partly due to fatal accidents involving female cyclists, who weren’t on Boris bikes. In fact the study showed how safe the London scheme has been, according to one of its authors, Dr Anna Goodman. “Our findings suggest that cycle hire users are certainly not at higher risk than other cyclists.”
In future, it’s likely that even more insights will be gleaned by combining data from cycling schemes with other statistics and maps, and also from more precise information on exactly which streets the cyclists use. Barclays Cycle Hire has ruled out incorporating GPS chips on bikes, due to sensitivities over privacy. But it, along with other schemes, is planning a smartphone app for easy payment. Perhaps, in the future, civic-minded users might consent for their phones to be tracked. In doing so, the safest streets could be identified and the riskiest roads improved for the benefit of all cyclists.