Games like SimCity are helping us build the cities of the future. Here's how

A visitor plays SimCity 2000 in New York's Museum of Modern Art. Image: Getty.

By 2050, the United Nations predicts that around 66 per cent of the world’s population will be living in urban areas, with the greatest expansion happening in developing regions such as Africa and Asia. Cities in these parts will be challenged to meet the needs of their residents, and provide sufficient housing, energy, waste disposal, healthcare, transportation, education and employment.

So, understanding how cities will grow – and how we can make them smarter and more sustainable along the way – is a high priority among researchers and governments the world over. We need to get to grips with the inner mechanisms of cities, if we’re to engineer them for the future.

Fortunately, there are tools to help us do this. And even better, using them is a bit like playing SimCity.


A whole new (simulated) world

Cities are complex systems. Increasingly, scientists studying cities have gone from thinking about “cities as machines”, to approaching “cities as organisms”. Viewing cities as complex, adaptive organisms – similar to natural systems like termite mounds or slime mould colonies – allows us to gain unique insights into their inner workings. Here’s how.

Complex organisms are characterised by individual units that can be driven by a small number of simple rules. As these relatively simple things live and behave, the culmination of all their individual interactions and behaviours generate more widespread aggregate phenomena.

For example, the beautiful and complex patterns made by flocking birds are not organised by a leader. They come about because each bird follows some very simple rules about how close to get to each other, which direction to fly in, and how to avoid predators.

Similarly, ant colonies can exhibit very sophisticated and seemingly intelligent behaviour – but this sophistication doesn’t come about as a result of a good leader. It is the result of lots of ants following relatively simple rules, without any regard for the bigger picture. It is easy to see how this perspective could be applied to human systems to explain phenomena like traffic jams.

So, if cities are like organisms, it follows that we should examine them from the bottom-up, and seek to understand how unexpected large-scale phenomena emerge from individual-level interactions. Specifically, we can simulate how the behaviour of individual “agents” – whether they are people, households, or organisations – affect the urban environment, using a set of techniques known as “agent-based modelling”.

Using The Sims to build your own city. Image: haljackey/Flickr, CC BY.

This is where it gets a bit like SimCity. It’s apt that the computer game was originally based on the work of Jay Forrester, a world-renown system scientist with an interest in urban dynamics. In the game, individual agents are given their own characteristics and rules, and allowed to interact with other agents and the environment. Different behaviour emerges through these interactions and drives the next set of interactions.

But while computer games can use generalisations about how people and organisations behave, researchers have to mine available data sets to construct realistic and robust rule sets, which can be rigorously tested and evaluated. To do this effectively, we need lots of data at the individual level.


Modelling from big data

These days, increases in computing power and the proliferation of big data give agent-based modelling unprecedented power and scope.

One of the most exciting developments is the potential to incorporate people’s thoughts and behaviours. In doing so, we can begin to model the impacts of people’s choices on present circumstances, and the future.

For example, we might want to know how changes to the road layout might affect crime rates in certain areas. By modelling the activities of individuals who might try to commit a crime, we can see how altering the urban environment influences how people move around the city, the types of houses that they become aware of, and consequently which places have the greatest risk of becoming the targets of burglary.

To fully realise the goal of simulating cities in this way, models need a huge amount of data. For example, to model the daily flow of people around a city, we need to know what kinds of things people spend their time doing, where they do them, who they do them with, and what drives their behaviour.

Without good-quality, high-resolution data, we have no way of knowing whether our models are producing realistic results. Big data could offer researchers a wealth of information to meet these twin needs. The kinds of data that are exciting urban modellers include:

  • Electronic travel cards that tell us how people move around a city.
  • Twitter messages that provide insight into what people are doing and thinking.
  • The density of mobile telephones that hint at the presence of crowds.
  • Loyalty and credit-card transactions to understand consumer behaviour.
  • Participatory mapping of hitherto unknown urban spaces, such as Open Street Map.

These data can often be refined to the level of a single person. As a result, models of urban phenomena no longer need to rely on assumptions about the population as a whole – they can be tailored to capture the diversity of a city full of individuals, who often think and behave differently from one another.

Missing people

There are, of course, serious practical and ethical considerations to take into account when integrating big data into urban models. The volume of background noise in new data sources can make it difficult to extract useful and reliable information. For example, it can often be difficult to distinguish Twitter messages posted by bots from those by real people.


We must also make sure that we understand who is well-represented in our data, and who is not. The digital divide is alive and well, and research suggests a class divide separating those who do and do not produce digital content. This means that there are probably large sections of the population missing from data sets.

We also need to find new ways of making these methods ethical. Traditionally, consumer and research ethics have been structured around informed consent: before taking part in interviews or surveys, participants need to sign consent forms that give the researchers permission to use their data. But now, individuals are digitising aspects of their lives such as moods, thoughts, feelings, and behaviours that have historically gone undocumented. And, importantly, these are often released publicly on the internet.

What's more, while an individual might have ticked a box that gives permission for their data to be used, that’s no guarantee that they’ve read and understood the terms. iTunes' June 2015 terms and conditions, for example, are more than 20,000 words long (20 times the length of this article). Researchers and service providers need to ask themselves how many people really get to grips with these documents, and whether their agreement fulfils our idea of consent.

We may never be able to simulate every individual in a city, and we’ll probably never want to. But we are getting closer to being able to simulate the richness of the fabric that weaves together to shape our cities. If we can do this, then we will be able to provide useful input on how best to shape cities in the future – perhaps even down to the last street light, bus and block of flats.The Conversation

Alison Heppenstall is an associate professor in geocomputation, and Nick Malleson a lecturer in geographical information systems, at the University of Leeds.

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

 
 
 
 

To build its emerging “megaregions”, the USA should turn to trains

Under construction: high speed rail in California. Image: Getty.

An extract from “Designing the Megaregion: Meeting Urban Challenges at a New Scale”, out now from Island Press.

A regional transportation system does not become balanced until all its parts are operating effectively. Highways, arterial streets, and local streets are essential, and every megaregion has them, although there is often a big backlog of needed repairs, especially for bridges. Airports for long-distance travel are also recognized as essential, and there are major airports in all the evolving megaregions. Both highways and airports are overloaded at peak periods in the megaregions because of gaps in the rest of the transportation system. Predictions for 2040, when the megaregions will be far more developed than they are today, show that there will be much worse traffic congestion and more airport delays.

What is needed to create a better balance? Passenger rail service that is fast enough to be competitive with driving and with some short airplane trips, commuter rail to major employment centers to take some travelers off highways, and improved local transit systems, especially those that make use of exclusive transit rights-of-way, again to reduce the number of cars on highways and arterial roads. Bicycle paths, sidewalks, and pedestrian paths are also important for reducing car trips in neighborhoods and business centers.

Implementing “fast enough” passenger rail

Long-distance Amtrak trains and commuter rail on conventional, unelectrified tracks are powered by diesel locomotives that can attain a maximum permitted speed of 79 miles per hour, which works out to average operating speeds of 30 to 50 miles per hour. At these speeds, trains are not competitive with driving or even short airline flights.

Trains that can attain 110 miles per hour and can operate at average speeds of 70 miles per hour are fast enough to help balance transportation in megaregions. A trip that takes two to three hours by rail can be competitive with a one-hour flight because of the need to allow an hour and a half or more to get to the boarding area through security, plus the time needed to pick up checked baggage. A two-to-three-hour train trip can be competitive with driving when the distance between destinations is more than two hundred miles – particularly for business travelers who want to sit and work on the train. Of course, the trains also have to be frequent enough, and the traveler’s destination needs to be easily reachable from a train station.

An important factor in reaching higher railway speeds is the recent federal law requiring all trains to have a positive train control safety system, where automated devices manage train separation to avoid collisions, as well as to prevent excessive speeds and deal with track repairs and other temporary situations. What are called high-speed trains in the United States, averaging 70 miles per hour, need gate controls at grade crossings, upgraded tracks, and trains with tilt technology – as on the Acela trains – to permit faster speeds around curves. The Virgin Trains in Florida have diesel-electric locomotives with an electrical generator on board that drives the train but is powered by a diesel engine. 

The faster the train needs to operate, the larger, and heavier, these diesel-electric locomotives have to be, setting an effective speed limit on this technology. The faster speeds possible on the portion of Amtrak’s Acela service north of New Haven, Connecticut, came after the entire line was electrified, as engines that get their power from lines along the track can be smaller and much lighter, and thus go faster. Catenary or third-rail electric trains, like Amtrak’s Acela, can attain speeds of 150 miles per hour, but only a few portions of the tracks now permit this, and average operating speeds are much lower.

Possible alternatives to fast enough trains

True electric high-speed rail can attain maximum operating speeds of 150 to 220 miles per hour, with average operating speeds from 120 to 200 miles per hour. These trains need their own grade-separated track structure, which means new alignments, which are expensive to build. In some places the property-acquisition problem may make a new alignment impossible, unless tunnels are used. True high speeds may be attained by the proposed Texas Central train from Dallas to Houston, and on some portions of the California High-Speed Rail line, should it ever be completed. All of the California line is to be electrified, but some sections will be conventional tracks so that average operating speeds will be lower.


Maglev technology is sometimes mentioned as the ultimate solution to attaining high-speed rail travel. A maglev train travels just above a guideway using magnetic levitation and is propelled by electromagnetic energy. There is an operating maglev train connecting the center of Shanghai to its Pudong International Airport. It can reach a top speed of 267 miles per hour, although its average speed is much lower, as the distance is short and most of the trip is spent getting up to speed or decelerating. The Chinese government has not, so far, used this technology in any other application while building a national system of long-distance, high-speed electric trains. However, there has been a recent announcement of a proposed Chinese maglev train that can attain speeds of 375 miles per hour.

The Hyperloop is a proposed technology that would, in theory, permit passenger trains to travel through large tubes from which all air has been evacuated, and would be even faster than today’s highest-speed trains. Elon Musk has formed a company to develop this virtually frictionless mode of travel, which would have speeds to make it competitive with medium- and even long-distance airplane travel. However, the Hyperloop technology is not yet ready to be applied to real travel situations, and the infrastructure to support it, whether an elevated system or a tunnel, will have all the problems of building conventional high-speed rail on separate guideways, and will also be even more expensive, as a tube has to be constructed as well as the train.

Megaregions need fast enough trains now

Even if new technology someday creates long-distance passenger trains with travel times competitive with airplanes, passenger traffic will still benefit from upgrading rail service to fast-enough trains for many of the trips within a megaregion, now and in the future. States already have the responsibility of financing passenger trains in megaregion rail corridors. Section 209 of the federal Passenger Rail Investment and Improvement Act of 2008 requires states to pay 85 percent of operating costs for all Amtrak routes of less than 750 miles (the legislation exempts the Northeast Corridor) as well as capital maintenance costs of the Amtrak equipment they use, plus support costs for such programs as safety and marketing. 

California’s Caltrans and Capitol Corridor Joint Powers Authority, Connecticut, Indiana, Illinois, Maine’s Northern New England Passenger Rail Authority, Massachusetts, Michigan, Missouri, New York, North Carolina, Oklahoma, Oregon, Pennsylvania, Texas, Vermont, Virginia, Washington, and Wisconsin all have agreements with Amtrak to operate their state corridor services. Amtrak has agreements with the freight railroads that own the tracks, and by law, its operations have priority over freight trains.

At present it appears that upgrading these corridor services to fast-enough trains will also be primarily the responsibility of the states, although they may be able to receive federal grants and loans. The track improvements being financed by the State of Michigan are an example of the way a state can take control over rail service. These tracks will eventually be part of 110-mile-per-hour service between Chicago and Detroit, with commitments from not just Michigan but also Illinois and Indiana. Fast-enough service between Chicago and Detroit could become a major organizer in an evolving megaregion, with stops at key cities along the way, including Kalamazoo, Battle Creek, and Ann Arbor. 

Cooperation among states for faster train service requires formal agreements, in this case, the Midwest Interstate Passenger Rail Compact. The participants are Illinois, Indiana, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, and Wisconsin. There is also an advocacy organization to support the objectives of the compact, the Midwest Interstate Passenger Rail Commission.

States could, in future, reach operating agreements with a private company such as Virgin Trains USA, but the private company would have to negotiate its own agreement with the freight railroads, and also negotiate its own dispatching priorities. Virgin Trains says in its prospectus that it can finance track improvements itself. If the Virgin Trains service in Florida proves to be profitable, it could lead to other private investments in fast-enough trains.

Jonathan Barnett is an emeritus Professor of Practice in City and Regional Planning, and former director of the Urban Design Program, at the University of Pennsylvania. 

This is an extract from “Designing the Megaregion: Meeting Urban Challenges at a New Scale”, published now by Island Press. You can find out more here.