How many tube lines does London have? A riposte

Some trains. Image: Chris McKenna/Wikipedia.

In this week’s CityMetric podcast, Jonn and I fell out over how many Tube lines there were.

TfL believes there to be 11 – the Bakerloo, Central, Circle, District, Hammersmith & City, Jubilee, Metropolitan, Northern, Piccadilly, Victoria, Waterloo & City lines. 

In my view, there are 14 – those 11 plus the Docklands Light Railway, the London Overground and Thameslink lines. (Keen Skylines listeners will know that I forgot the existence of Thameslink and argued for a mere 13, but that’s by the by.)

In Jonn’s view, there should be 13 lines. Besides the canonical 11, he believes that the District and Northern lines should be treated as four lines, not two.

Now he has written a lengthy piece explaining his thoughts on the number of Tube lines at greater detail. I’ll take those in the reverse order to Jonn, who deals first with his eccentric beliefs about the District and Northern Lines and then onto his view that the DLR, Thameslink and Overground do not count as Tube lines.

To take the Tube lines point first: Jonn’s argument is a good one, but, regrettably, not for the case he wishes to make. He correctly identifies two internally consistent definitions of what constitutes a Tube line. The first, what you might call the “Narrow & Nerdish” definition restricts the meaning of what a Tube line is to the “deep-level” trains, that is, the one that look like Tube.

That definition would restrict the number of Tube lines to seven: the Bakerloo, Central, Jubilee, Northern, Piccadilly, Victoria and Waterloo & City lines. 

This makes sense. These trains can run interchangeably on their routes without modification (mostly - ed.), have the same technical limitations and designs, and look the same. This is a perfectly reasonable definition of the Tube.

The second definition, what you might call the “Generous & Geeky” reading of how many Tube lines there are expands to include a number of routes that are not, strictly speaking, deep-level Tube lines. Under the guise of following this second definition, Jonn defines the Tube lines as the canonical 11, plus his additional District and Northern Lines, on which subject I’ll go into further detail below.

This makes no sense.  Both in terms of its speed, design, capacity and abilities, a Metropolitan, District or Hammersmith & City Line train has more in common with the Thameslink or Overground fleets than the Central Lines. There is no case to count the Metropolitan Line but not Thameslink or the District Line.

You can make a passable case for not including the Docklands Light Railway as it is a different type of rolling stock entirely, but once you have expanded the definition you might as well include the DLR as well.

There are two definitions that work: one that counts only the deep-level lines and one which counts any of the subterranean railways on TfL’s map. Jonn is trying to have his cake and eat it, proving that he who battles Brexiteers must take great care, lest he become a Brexiteer himself.

What about Jonn’s other argument, that the District and Northern Line are not two lines, but four?

Let’s take the case for splitting the District Line first. Here, for reference, is the District Line as it is:


Jonn argues that it should be split into two. Let’s call this one the Stephen’s Supreme Line. It’s just a name.


Click to expand.

And the second, which would look like this, which we’ll call, for argument’s sake, the Elledge’s Egregious Express Railway:

Click to expand.

The thing about the Egregious Express is it makes sense if you live on the Edgware Road and commute to Wimbledon, or vice versa. But for Jonn’s argument to work, someone living at Wimbledon and working at Westminster would, currently, have to get off at Earl’s Court and change from the Egregious Express to the Stephen Supreme.

But of course, they don’t. They carry on on a regular District Line train. It makes far more sense to think of this route as a series of interweaving branches, rather than a full-fledged line.

(Editor’s note: Stephen seems unaware that trains from Wimbledon run to Edgware Road and Westminster. I put this point to him during the editing process, but he didn’t want to hear it, so I left this in.)

(Further editor's note: A reader points out that I'd misread Stephen's original point. He's right. That's really annoying. On the upside, I did at least correct his earlier contention that 11+3=13.)

What of the Northern Line? This argument is rather better than the case for two District Lines. In practice, the Northern Line operates almost as two lines now, a divide that TfL expects to formalise. So there is, at a pinch, a case to be made for the number of train lines being seven or 14 – but not the 13 that Jonn believes.

There's a whole podcast on this if you fancy it.

Stephen Bush is special correspondent at our parent title, the New Statesman. His daily briefing, Morning Call, provides a quick and essential guide to British politics.

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Smart cities need to be more human, so we’re creating Sims-style virtual worlds

The Sims 2 on show in 2005. Image: Getty.

Huge quantities of networked sensors have appeared in cities across the world in recent years. These include cameras and sensors that count the number of passers by, devices to sense air quality, traffic flow detectors, and even bee hive monitors. There are also large amounts of information about how people use cities on social media services such as Twitter and foursquare.

Citizens are even making their own sensors – often using smart phones – to monitor their environment and share the information with others; for example, crowd-sourced noise pollution maps are becoming popular. All this information can be used by city leaders to create policies, with the aim of making cities “smarter” and more sustainable.

But these data only tell half the story. While sensors can provide a rich picture of the physical city, they don’t tell us much about the social city: how people move around and use the spaces, what they think about their cities, why they prefer some areas over others, and so on. For instance, while sensors can collect data from travel cards to measure how many people travel into a city every day, they cannot reveal the purpose of their trip, or their experience of the city.

With a better understanding of both social and physical data, researchers could begin to answer tough questions about why some communities end up segregated, how areas become deprived, and where traffic congestion is likely to occur.

Difficult questions

Determining how and why such patterns will emerge is extremely difficult. Traffic congestion happens as a result of personal decisions about how to get from A to B, based on factors such as your stage of life, your distance from the workplace, school or shops, your level of income, your knowledge of the roads and so on.

Congestion can build locally at pinch points, placing certain sections of the city’s transport networks under severe strain. This can lead to high levels of air pollution, which in turn has a severe impact on the health of the population. For city leaders, the big question is, which actions – imposing congestion charges, pedestrianising areas or improving local infrastructure – would lead to the biggest improvements in both congestion, and public health.

We know where – but why? Image: Worldoflard/Flickr/creative commons.

The irony is, although modern technology has the power to collect vast amounts of data, it doesn’t always provide the means to analyse it. This means that scientists don’t have the tools they need to understand how different factors influence the way cities function and grow. Here, the technique of agent-based modelling could come to the rescue.

The simulated city

Agent-based modelling is a type of computer simulation, which models the behaviour of individual people as they move around and interact inside a virtual world. An agent-based model of a city could include virtual commuters, pedestrians, taxi drivers, shoppers and so on. Each of these individuals has their own characteristics and “rules”, programmed by researchers, based on theories and data about how people behave.

After combining vast urban datasets with an agent-based model of people, scientists will have the capacity to tweak and re-run the model, until they detect the phenomena they’re wanting to study – whether it’s traffic jams or social segregation. When they eventually get the model right, they’ll be able to look back on the characteristics and rules of their virtual citizens, to better understand why some of these problems emerge, and hopefully begin to find ways to resolve them.

For example, scientists might use urban data in an agent-based model to better understand the characteristics of the people who contribute to traffic jams – where they have come from, why they are travelling, what other modes of transport they might be willing to take. From there, they might be able to identify some effective ways of encouraging people to take different routes or modes of transport.

Seeing the future

Also, if the model works well in the present time, then it might be able to produce short-term forecasts. This would allow scientists to develop ways of reacting to changes in cities, in real time. Using live urban data to simulate the city in real-time could help to inform the managers of key services during periods of major disruption, such as severe weather, infrastructure failure or evacuation.

Using real-time data adds another layer of complexity. But fortunately, other scientific disciplines have also been making advances in this area. Over decades, the field of meteorology has developed cutting-edge mathematical methods, which allow their weather and climate models to respond to new weather data, as they arise in real time.

The ConversationThere’s a lot more work to be done before these methods from meteorology can be adapted to work for agent-based models of cities. But if they’re successful, these advancements will allow scientists to build city simulations which are driven by people - and not just the data they produce.

Nick Malleson, Associate Professor of Geographical Information Systems, University of Leeds and Alison Heppenstall, Professor in Geocomputation, University of Leeds.

This article was originally published on The Conversation. Read the original article.