Can you teach an old dog new tricks? Not on the London Underground

Artistic shot to helpfully indicate that this article is primarily about escalators. Image: Tom Page

How do you teach an old dog new tricks?

On the London Underground, they’ve resorted to pleading. “Please stand on both sides of the escalator for better efficiency during refurbishment works!” – so cried a sign at Oxford Circus station the other week.

People read it, before diligently filing to stand the right and leave the left side free for walkers.

Having everyone stand still on the escalator is a more efficient way to empty a station at rush hour – Transport for London is right about this.

So why won’t we just do it? The problem is that TfL is up against Tube etiquette, and for Londoners, anything else is anarchy or tourism.

Habits are hard to break.

A 2016 study from Duke University suggests habits leave lasting marks on circuits in the brain, priming us to repeat an action long after it stops being beneficial. So depending on your outlook, Londoners are either shaming or priding themselves by refusing to comply with requests to stand on both sides – now as well as during escalator trials in 2015 and 2016.

The Holborn experiment worked as long as Tube staff were physically present to enforce it, facing off swearing, showing, and eye rolling. Without supervision, the travelling public quickly returned to the habit of a lifetime.

The escalators at Holborn station. Image: Renaissance Chambara/creative commons.

TfL learned two lessons at Holborn, according to a Freedom of Information request by Gizmodo. Firstly, it’s really hard to change crowd behaviour. Secondly, we really should be trying, as it would be better for all of us. The tube network is bursting at the seams, and having people stand instead of walk could mean station capacity increasing by about 30%.

The specifics depend on the number of people and the length of the escalator, although TfL summed it up nicely to CityMetric at the time of the initial test: "We get a lot of congestion at the bottom because the majority of customers don't want to walk. The left hand side empty, while everyone is queuing up to stand on the right. By filling up both sides, we can actually carry more people more quickly and clear that congestion."

You’d think this is an idea that Londoners could get behind: do this simple thing you’ll get in and out of the station faster.

And better yet: the station becomes far less likely to temporarily close to prevent overcrowding, meaning we won’t be piling up outside a closed station door. But TfL aren’t planning any more trials, in large part because it’s just too hard to get people to cooperate.

Beautifully shiny, empty escalators. Image: Tom Page/creative commons.

This may seem ridiculous, but as all logical minds eventually discover: rationality isn’t always the motivating force.

Habit is just one factor here – Londoners have come to see the idea of walking on the left as a signifier of belonging, to the point where being elbowed for standing on the wrong side may be considered a genuine London tourist experience. And besides, walking on one side of the escalator feels like it should be more efficient, right?

Now, if you’re ready to dismiss this whole escalator business as “a London thing”, consider how most airlines board their planes back to front, despite evidence proving this is the slowest method of all.

That may seem counter-intuitive, but when everyone boarding together has to go to the same ten rows, they pile up behind each other and everything slows down.

Paddington, with those fun stairs you can run up. Image: Chris McKenna/creative commons.

When the Discovery Channel tested out various boarding methods, back-to-front boarding was found to be the slowest, taking 25 minutes to board 173 people. Far quicker was random boarding to assigned seats, which took just 17 minutes.

Part of the problem is that we have so much carry-on baggage now that airlines charge for check-in luggage, resulting in airplane boarding times having more than doubled since 1970. But while people still have to put their bags away during random boarding, at least they’re spread out along the entire length of the plane.

Airlines are aware of these facts too, and it’s expensive to idle on the runway. So why are we still boarding from the back of the plane?

One of the reasons is that people really don’t like random boarding, apparently finding it “frustrating” or "confusing (a fact that’s confusing in its own right - we’re talking about sitting down in a plane here).

But it shows that London commuters aren’t that unusual. Once we have an idea about the best way to do something, it’s hard to change, even in the face of evidence to the contrary.

Not to mention how the pleasure of habit is often its own reward.

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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.