If ‘smart’ is the solution, what exactly is the problem?

Most adepts of the smart city-idea suggest a tight link between technology and the wellbeing of the citizens, symbolizing a new kind of technology-led urban utopia. They promise the solution to many urban problems, including crime, traffic congestion, inefficient services and economic stagnation, or a healthy life for all. 

Siemens makes the strongest and most explicit statement of the philosophical underpinnings of the smart-city: Several decades from now cities will have countless autonomous, intelligently functioning IT systems that will have perfect knowledge of users’ habits and energy consumption and provide optimum service…The goal of such a city is to optimally regulate and control resources by means of autonomous IT systems[1].

It is unmistakably that business leaders, having in mind a multi-billion smart city technologies market overstate the benefits of technology, despite many examples that prove otherwise. Therefore, according to The Economist it is not surprising that a ‘techlash’ is underway: The monopolistic dominance of behemoths like Google, Amazon and Facebook and their treatment of sensitive data, the lack of transparency and accountability of algorithm-based decision making, the aversion of the gig economy are major drivers.  

Neglecting the human component is by far the worst mistake any aspiring smart city can make. If these future smart cities aim for efficiency, they just cannot be planned without the community. Robert Holland wrote: The real smart city has to begin to think with its collective social and political brain, rather than through its technological tools….. It is made up of myriads of initiatives where technology is used to empower community networks, to monitor equal access to urban infrastructures or scale up new forms of sustainable living

A human-centric turn of the smart city narrative starts from the problems that citizens and their representatives experience. Then possible solutions are discussed and finally these solutions are specified, the role of technology included. 

This post is based on the new e-book Better cities, the contribution of digital technology.  Interested? Download the book here for free (90 pages)

Content:

Hardcore: Technology-centered approaches

1. Ten years of smart city technology marketing

2. Scare off the monster behind the curtain: Big Tech’s monopoly

Towards a humancentric approach

3. A smart city, this is how you do it

4. Digital social innovation: For the social good

Misunderstanding the use of data

5. Digital twins

6. Artificial intelligence

Embedding digitization in urban policy

7. The steps to urban governance

8. Guidelines for a responsible digitization policy

9. A closer look at the digitization agenda of Amsterdam

10. Forging beneficial cooperation with technology companies

Applications

11. Government: How digital tools help residents regaining power?

12. Mobility: Will MaaS reduce the use of cars?

13. Energy: Smart grids – where social and digital innovation meet

14. Healthcare: Opportunities and risks of digitization

Wrapping up: Better cities and technology

15. Two 100 city missions: India and Europe

Epilogue: Beyond the Smart City


[1] Cited in: Adam Greenfeld: Against the smart city. A pamphlet

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Green and smart are not twins

Recently, the European Commission launched a 100-city plan, the EU Mission on Climate-Neutral and Smart Cities. One hundred European cities that aspire to be climate neutral by 2030 were invited to register and count on supplemental funding. And yes, more than 100 did. As the European Commission aspires a simultaneous green and digital transformation, it is taking about green and digital twin. I immediately thought of another 100-city plan, India’s Smart City Mission. In 2015, Prime Minister Modi announced that in six years 100 Indian cities would become ‘smart’ with the goal of solving the uncountable problems that Indian cities face. This connection failed. Therefore, my advice to the commission is, stick to one ambition, becoming climate-neutral.

The main reason of the limited outcomes of the Indian mission is the gap between its ambitions and the nature of the problems that India is facing. Cities are bursting at the seams because of the millions of poor people who flock to cities every year in search of work and a place to live that find them only in the growing slums. The priorities for which the country must find a solution are therefore: improving life in rural areas, improving the quality of housing, ensuring safe drinking water, waste disposal, sanitation, and purification of wastewater, good transport and less polluting car traffic. 

The ‘Mission’ has not tackled these problems at the root, but instead looked for a solution in ‘smartification’.

IC solutions have been concentrated in enclaves where businesses and prosperous citizens are welcomed. The Government of India Special Rapporteur on Housing therefore notes that the proposals submitted had a predominant focus on technology rather than prioritizing affordable housing and doubts the correctness of this choice. Instead of emphasizing the role of digital technology, the focus should have been on equitable, inclusive, and sustainable living areas for all. 

The European Union cherishes the image of a ‘green and digital twin’, a simultaneous green and digital transformation. Digital technology will certainly contribute to the energy transition, for example in ‘smart grids’. However, the reduction of greenhouse gases and digitization should not be seen as extensions of each other. Making a city climate neutral requires much more than (digital) technology, certainly if this aim must be achieved before 2030. This is only possible by focusing on the basics: building wind-turbines, spreading VF-panels, adapting the grid and organize the availability of green hydrogen and finally cherish citizen’ participation.

This post based on by the new e-book Better cities, the contribution of digital technology.  Interested? Download the book here for free (90 pages)

Content:

Hardcore: Technology-centered approaches

1. Ten years of smart city technology marketing

2. Scare off the monster behind the curtain: Big Tech’s monopoly

Towards a humancentric approach

3. A smart city, this is how you do it

4. Digital social innovation: For the social good

Misunderstanding the use of data

5. Digital twins

6. Artificial intelligence

Embedding digitization in urban policy

7. The steps to urban governance

8. Guidelines for a responsible digitization policy

9. A closer look at the digitization agenda of Amsterdam

10. Forging beneficial cooperation with technology companies

Applications

11. Government: How digital tools help residents regaining power?

12. Mobility: Will MaaS reduce the use of cars?

13. Energy: Smart grids – where social and digital innovation meet

14. Healthcare: Opportunities and risks of digitization

Wrapping up: Better cities and technology

15. Two 100 city missions: India and Europe

Epilogue: Beyond the Smart City

Forget the ***city

In 2009, IMB launched a global marketing campaign around the previously little-known concept of ‘smart city’ with the aim of making city governments receptive to ICT applications in the public sector. The initial emphasis was on process control. Emerging countries were interested in the first place: Many made plans to build smart cities ‘from scratch’, in the first place to attract foreign investors. The Korean city of Songdo, developed by Cisco and Gale International, is a well-known example. 

The emphasis soon shifted from process control to using data from the residents themselves. Google wanted to supplement its already rich collection of data with data that city dwellers provide with their mobile phones to create a range of new commercial applications. Its sister company Sidewalk Labs, which was set up for that purpose, started developing a pilot project in Toronto. That failed, partly due to the growing resistance to the prospective violation of privacy. This opposition has had global repercussions and resulted in many countries in legislation to protect privacy. China and cities in Southeast Asia – where Singapore is leading the way – ignored this criticism.

The rapid development of digital technologies, such as artificial intelligence, gave further impetus to discussion about the ethical implications of technology. Especially in the US, applications in facial recognition and predictive police were heavily criticized.

This current situation – particularly in the Netherlands – can be characterized on the one hand by the development of regulations to safeguard ethical principles and on the other by the search for responsible applications of digital technology.

The question is therefore why we should still talk about smart cities. Touria Meliani, alderman of Amsterdam, prefers to speak of ‘wise city’ than of ‘smart city’ to emphasize that she is serious about putting people first. But instead of introducing other adjectives, skipping them all is better.

The best way to understand human life in the city is respecting the complexity of the city and life within it.

Precisely because of the complexity of the city, the use of reductionist adjectives such as ‘smart’, ‘sharing’, ‘circular’, ‘climate neutral’, ‘resilient’. ‘inclusive’ – even my own favorite ‘humane’ – is better avoided. The doughnut-principle is the best way to analyze the city from different perspectives and to define the way people can live in a social and ecological sustainable way, the use of digital technology included.

This post based on by the new e-book Better cities, the contribution of digital technology.  Interested? Download the book here for free (90 pages)

Content:

Hardcore: Technology-centered approaches

1. Ten years of smart city technology marketing

2. Scare off the monster behind the curtain: Big Tech’s monopoly

Towards a humancentric approach

3. A smart city, this is how you do it

4. Digital social innovation: For the social good

Misunderstanding the use of data

5. Digital twins

6. Artificial intelligence

Embedding digitization in urban policy

7. The steps to urban governance

8. Guidelines for a responsible digitization policy

9. A closer look at the digitization agenda of Amsterdam

10. Forging beneficial cooperation with technology companies

Applications

11. Government: How digital tools help residents regaining power?

12. Mobility: Will MaaS reduce the use of cars?

13. Energy: Smart grids – where social and digital innovation meet

14. Healthcare: Opportunities and risks of digitization

Wrapping up: Better cities and technology

15. Two 100 city missions: India and Europe

Epilogue: Beyond the Smart City

Democracy beyond voting

There is a widespread desire among citizens for greater involvement in political decision-making than cast a vote periodically. The participation ladder, developed in 1969 by Sherry Arnstein, is a useful summary of the degree in which government and citizens share power. Below, I jump to the highest rung of the ladder: Local government empowers citizens to make independent decisions, or partial autonomy.

In Italy this process has boomed, and the city of Bologna has become a stronghold of so-called urban commons. Citizens become designers, managers, and performers of selected municipal tasks, such as creating green areas, converting an empty house into affordable units, operating a minibus service, cleaning, and maintaining the city walls, refurbishing parts of the public space, keeping open a swimming-pool and much more. 

The most important instruments are cooperation-pacts. In each pact, city authorities and the parties involved (informal groups, NGOs, schools, entrepreneurs) lay down agreements about their activities, responsibilities, and power. Hundreds of pacts have been signed since the regulation was adopted in 2011. The city provides what the citizens need – money, material, housing, advice – and the citizens make their time, skills, and organizational capacity available. 

The commons-movement will influence urban governance in the longer term. The Italian political scientistChristian Iaione predicts the emergence of a city of commons. Here, many urban tasks are performed by commons and cooperatives. The city then is a network of both, decision-making is decentralized and deconcentrated.

A similar idea The city as a platform has emerged in the US coming from a completely different direction. Instead of simply voting every few years and leaving city administration to elected officials and expert bureaucrats, the networked city sees citizens as co designers, co-producers, and co-learners, according to Stefaan Verhulst, co-founder of GovLab. In the city as a platform residents look individually and collectively for new and better ways to meet their needs and enliven public life. These may be neighborhood-based initiatives, for example the redevelopment of a neighborhood or city-wide initiatives, for example a cooperative of taxi drivers, competing with Uber.

Without saying it in so many words, everyone involved sees both the city of commons and the city as a platform as a long-term opportunity to make citizens the engine of urban development instead of enabling multinational companies taking over that role. For the time being, city administrators can best focus on enabling and supporting citizens’ joint action to make cities more beautiful, liveable, and sustainable.

This post based on by the new e-book Better cities, the contribution of digital technology.  Interested? Download the book here for free (90 pages)

Content:

Hardcore: Technology-centered approaches

1. Ten years of smart city technology marketing

2. Scare off the monster behind the curtain: Big Tech’s monopoly

Towards a humancentric approach

3. A smart city, this is how you do it

4. Digital social innovation: For the social good

Misunderstanding the use of data

5. Digital twins

6. Artificial intelligence

Embedding digitization in urban policy

7. The steps to urban governance

8. Guidelines for a responsible digitization policy

9. A closer look at the digitization agenda of Amsterdam

10. Forging beneficial cooperation with technology companies

Applications

11. Government: How digital tools help residents regaining power?

12. Mobility: Will MaaS reduce the use of cars?

13. Energy: Smart grids – where social and digital innovation meet

14. Healthcare: Opportunities and risks of digitization

Wrapping up: Better cities and technology

15. Two 100 city missions: India and Europe

Epilogue: Beyond the Smart City

Data is not the new oil

I suggest that anybody who is talking about ‘big data’ and ‘data driven policy’ or using grotesque statements like data is the new oil to revisit the foundations of scientific research and the embedded vision on data. 

Without elementary insight in the way scientists arrive at their conclusions ‘data driven policy’ can have disastrous consequences. The city of Chattanooga has build a digital twin. That is a digital model that is connected to reality with the help of sensors. Such a dynamic model can be used for simulation purposes if the connections between the variables have been established. Here things can go wrong. In Chattanooga the model was used to simulate the impact of flexible lane assignment and traffic light phasing. It turned out that this could result in a 30% decrease of congestion.

Had this experiment been carried out in the real world, the result would probably have been disastrous. Traffic experts note time and again that every newly opened road gets satiated after a short time, while the traffic on other roads hardly decreases. In econometrics this phenomenon is called induced demand. In a study of urban traffic patterns between 1983 and 2003, economists Gilles Duranton and Matthew Turner found that car use increases proportionally with the growth of road capacity: Every road user reacts differently to the opening or closing of a road. Those reactions can be to move the ride to another time, to use a different road, to ride with someone else, to use public transport or to cancel the ride. To understand this pattern data must be collected from e sufficient large sample of road behavior of individual drivers. 

What the computer scientist in Chattanooga did wrong is assuming that only the adding of a single lane and changing the intervals of the traffic lights would cause all drivers’ behavior change into the same direction, as if they were metal balls, reacting upon a change in the magnetic field. If the ICT-experts had collaborated with traffic experts, the digital twin might have been fed with an empirical justifiable model, that incorporates the assumption of induced demand. 

In essence, data is useless without a theory, based on already established insights or views. 

This post based on by the new e-book Better cities, the contribution of digital technology.  Interested? Download the book here for free (90 pages) 

Content:

Hardcore: Technology-centered approaches

1. Ten years of smart city technology marketing

2. Scare off the monster behind the curtain: Big Tech’s monopoly

Towards a humancentric approach

3. A smart city, this is how you do it

4. Digital social innovation: For the social good

Misunderstanding the use of data

5. Digital twins

6. Artificial intelligence

Embedding digitization in urban policy

7. The steps to urban governance

8. Guidelines for a responsible digitization policy

9. A closer look at the digitization agenda of Amsterdam

10. Forging beneficial cooperation with technology companies

Applications

11. Government: How digital tools help residents regaining power?

12. Mobility: Will MaaS reduce the use of cars?

13. Energy: Smart grids – where social and digital innovation meet

14. Healthcare: Opportunities and risks of digitization

Wrapping up: Better cities and technology

15. Two 100 city missions: India and Europe

Epilogue: Beyond the Smart City

Bigg Tech’s monopoly

Two recent books deal with this problem in depth and call for tailored actions. These books are Shoshana Zuboff’s The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power (2019) and Cory Doctorow’s How to destroy surveillance capitalism (2021). Zuboff describes in detail how Google, Amazon and Facebook collect data with only one goal, to entice citizens to buy goods and services: 

Big Tech’s product is persuasion. The services — social media, search engines, maps, messaging, and more — are delivery systems for persuasion.

The unprecedented power of Big Tech is a result of the fact that these companies have become almost classic monopolies. Until the 1980s, the US had strict antitrust legislation: the Sherman’s act, notorious for big business. Ronald Reagan quickly wiped it out in his years as president, and Margaret Thatcher did the same in the UK, Brian Mulroney in Canada, and Helmut Kohl in Germany. While Sherman saw monopolies as a threat to the free market, Reagan believed that government interference threatens the free market. Facebook joins in if it sees itself as a ‘natural monopoly’: You want to be on a network where your friends are also. But you could also reach your friends if there were more networks that are interoperable. Facebook has used all economic, technical, and legal means to combat the latter, including takeover of potential competitors: Messenger, Instagram, and WhatsApp.

In the early 21st century, there was still a broad belief that emerging digital technology could lead to a better and more networked society.

Bas Boorsma: The development of platforms empowered start-ups, small companies, and professionals. Many network utopians believed the era of ‘creative commons’ had arrived and with it, a non-centralized and highly digital form of ‘free market egalitarianism’ (New Digital Deal, p.52). Nothing has come of this: Digitalization-powered capitalism now possesses a speed, agility and rawness that is unprecedented (New Digital Deal, p.54). Even the startup community is becoming one big R&D lab for Big Tech. Many startups hope to be acquired by one of the tech giants and then cash in on millions. As a result, Big Tech is on its way to acquire a dominant position in urban development, the health sector and education, in addition to the transport sector.

Thanks to its monopoly position, Big Tech can collect unlimited data, even if European legislation imposes restrictions and occasional fines. After all, a lot of data is collected without citizens objecting to it. Mumford had already realized this in 1967: Many consumers see these companies not only as irresistible, but also ultimately beneficial. These two conditions are the germ of what he called the megatechnics bribe.

The only legislation that can break the power of Big Tech is a strong antitrust policy, unbundling the companies, an absolute ban on acquisitions and rigorous taxation. In addition, governments should take back control of technological development, as they did until the end of the last century. Democratic control of the development of technology is an absolute precondition! 

This post based on by the new e-book Better cities, the contribution of digital technology.  Interested? Download the book here for free (90 pages)

Content:

Hardcore: Technology-centered approaches

1. Ten years of smart city technology marketing

2. Scare off the monster behind the curtain: Big Tech’s monopoly

Towards a humancentric approach

3. A smart city, this is how you do it

4. Digital social innovation: For the social good

Misunderstanding the use of data

5. Digital twins

6. Artificial intelligence

Embedding digitization in urban policy

7. The steps to urban governance

8. Guidelines for a responsible digitization policy

9. A closer look at the digitization agenda of Amsterdam

10. Forging beneficial cooperation with technology companies

Applications

11. Government: How digital tools help residents regaining power?

12. Mobility: Will MaaS reduce the use of cars?

13. Energy: Smart grids – where social and digital innovation meet

14. Healthcare: Opportunities and risks of digitization

Wrapping up: Better cities and technology

15. Two 100 city missions: India and Europe

Epilogue: Beyond the Smart City

The main shortcoming of AI is its lack of intelligence

Artificial intelligence is the ‘independent construction of pattern in large datasets by computers’. Still, people hold an important role in this. This role consists in the first place in writing an instruction – an algorithm – and then in the composition of a training set, a selection of many examples, for example of animals that are labeled as dog or cat and if necessary, lion or tiger and more. In essence, the computer looks for statistically significant similarities in whatever data, provided by the operator, to predict the probability that some relation will exist or phenomenon will occur.

If computers are learned to make judgement about people, things can go terribly wrong. The St. George Hospital Medical School in London has employed disproportionately many white males for at least a decade because the ‘learning set’ reflected the incumbent staff. The learning set itself represented the bias of those who selected it. 

The fight against crime in the United States, has been the scene of artificial intelligence’s abuse for years. The two most used techniques that resulted are predictive policing (PredPol) and facial recognition. In the case of predictive policing, patrols are given directions in which neighborhood or even street they should patrol at a given moment because computers have calculated that the risk of crimes (vandalism, burglary, violence) is highest then.

Predictive policing and facial recognition are based on a “learning set” of thousands of “suspicious” individuals. At one point, New York police had a database of 48,000 individuals. 66% of those were black, 31.7% were Latino and only 1% were white. The composition of this dataset was completely biases, and therefore the computerized ‘decisions’ ware biased too. Even worse is that the final ‘decisions’ made by the computer cannot be explained, and the underlying process is a blackbox. This is a serious ethical issue and the reason why many demand to forbit the application of artificial intelligence. 

Bias is not the only thing. In so-called fight against crime the computer calculates on request the chances that crime would happen at a certain time and in a certain place.  But if the client exchanged the dominant paradigm of identifying, prosecuting and incarcerating criminals for that of finding potential offenders in a timely manner and giving them the help, they need?  A large proportion of those arrested by the police in the US are addicted to drugs or alcohol and severely mentally disturbed.  The University of Chicago Data Science for Social Good Program used artificial intelligence to analyze a database of 127,000 people. The aim was to find out, based on historical data, which of those involved was most likely to be arrested within a month. This is not with the intention of hastening an arrest with predictive techniques, but instead to offer them targeted medical assistance. This program was picked up in several cities and in Miami it resulted in a 40% reduction in arrests and the closing of an entire prison.

AI means computer power. Intelligence resides in those who are using this power. Less biased application of artificial intelligence depends on the through-out choice of the connections in the learning sets. These connections must be scientifically validated and approved by scholars with different backgrounds instead of police officers or computer scientists.

This post is based on the new e-book Better cities, the contribution of digital technology.  Interested? Download the book here for free (90 pages)

Content:

Hardcore: Technology-centered approaches

1. Ten years of smart city technology marketing

2. Scare off the monster behind the curtain: Big Tech’s monopoly

Towards a humancentric approach

3. A smart city, this is how you do it

4. Digital social innovation: For the social good

Misunderstanding the use of data

5. Digital twins

6. Artificial intelligence

Embedding digitization in urban policy

7. The steps to urban governance

8. Guidelines for a responsible digitization policy

9. A closer look at the digitization agenda of Amsterdam

10. Forging beneficial cooperation with technology companies

Applications

11. Government: How digital tools help residents regaining power?

12. Mobility: Will MaaS reduce the use of cars?

13. Energy: Smart grids – where social and digital innovation meet

14. Healthcare: Opportunities and risks of digitization

Wrapping up: Better cities and technology

15. Two 100 city missions: India and Europe

Epilogue: Beyond the Smart City

Forget the ***city

In 2009, IMB launched a global marketing campaign around the previously little-known concept of ‘smart city’ with the aim of making city governments receptive to ICT applications in the public sector. The initial emphasis was on process control. Emerging countries were interested in the first place: Many made plans to build smart cities ‘from scratch’, in the first place to attract foreign investors. The Korean city of Songdo, developed by Cisco and Gale International, is a well-known example. 

The emphasis soon shifted from process control to using data from the residents themselves. Google wanted to supplement its already rich collection of data with data that city dwellers provide with their mobile phones to create a range of new commercial applications. Its sister company Sidewalk Labs, which was set up for that purpose, started developing a pilot project in Toronto. That failed, partly due to the growing resistance to the prospective violation of privacy. This opposition has had global repercussions and resulted in many countries in legislation to protect privacy.

The rapid development of digital technologies, such as artificial intelligence, gave further impetus to discussion about the ethical implications of technology. Especially in the US, applications in facial recognition and predictive police were heavily criticized.

This current situation – particularly in the Netherlands – can be characterized on the one hand by the development of regulations to safeguard ethical principles and on the other by the search for responsible applications of digital technology.

The question is therefore how useful the term ‘smart city’ is. Touria Meliani, alderman of Amsterdam, prefers to speak of ‘wise city’ than of ‘smart city’ to emphasize that she is serious about putting people first. But instead of introducing other adjectives, skipping them all is better.

The best way to understand human life in the city is respecting the complexity of the city and life within it. Exactly because of the city’s complexity, the use of reductionist adjectives such as ‘smart’, ‘sharing’, circular, climate-neutral’, ‘resilient’ is better omitted. The doughnut-principle is the best way to analyze the city from different perspectives and to define the way people can live in a social and ecological sustainable way, the use of digital technology included.

This post based on by the new e-book Better cities, the contribution of digital technology.  Interested? Download the book here for free

Collect meaningful data and stay away from dataism.

I am a happy user of a Sonos sound system. Nevertheless, the helpdesk must be involved occasionally. Recently, it knew within five minutes that my problem was the result of a faulty connection cable between the modem and the amplifier. As it turned out, the helpdesk was able to remotely generate a digital image of the components of my sound system and their connections and saw that the cable in question was not transmitting any signal. A simple example of a digital twin. I was happy with it. But where is the line between the sense and nonsense of collecting masses of data?

What is a digital twin

A digital twin is a digital model of an object, product, or process. In my training as a social geographer, I had a lot to do with maps, the oldest form of ‘twinning’. Maps have laid the foundation for GIS technology, which in turn is the foundation of digital twins. Geographical information systems relate data based on geographical location and provide insight into their coherence in the form of a model. If this model is permanently connected to reality with the help of sensors, then the dynamics in the real world and those in the model correspond and we speak of a ‘digital twin’. Such a dynamic model can be used for simulation purposes, monitoring and maintenance of machines, processes, buildings, but also for much larger-scale entities, for example the electricity grid.

From data to insight

Every scientist knows that data is indispensable, but also that there is a long way to go before data leads to knowledge and insight. That road starts even before data is collected. The first step is assumptions about the essence of reality and thus the possibility of knowing it. There has been a lot of discussion about this within the philosophy of science, from which two points of view have been briefly crystallized, a systems approach and a complexity approach.

The systems approach assumes that reality consists of a stable series of actions and reactions in which law-like connections can be sought. Today, almost everyone assumes that this only applies to physical and biological phenomena. Yet there is also talk of social systems. This is not a question of law-like relationships, but of generalizing assumptions about human behavior at a high level of aggregation. The homo economicus is a good example. Based on such assumptions, conclusions can be drawn about how behavior can be influenced.

The complexity approach sees (social) reality as the result of a complex adaptive process that arises from countless interactions, which – when it comes to human actions – are fed by diverse motives. In that case it will be much more difficult to make generic statements at a high level of aggregation and interventions will have a less predictable result.

Traffic models

Traffic policy is a good example to illustrate the distinction between a process and a complexity approach. Simulation using a digital twin in Chattanooga of the use of flexible lane assignment and traffic light phasing showed that congestion could be reduced by 30%. Had this experiment been carried out, the result would probably have been very different. Traffic experts note time and again that every newly opened road becomes full after a short time, while the traffic picture on other roads hardly changes. In econometrics this phenomenon is called induced demand. In a study of urban traffic patterns between 1983 and 2003, economists Gilles Duranton and Matthew Turner found that car use increases proportionally with the growth of road capacity. The cause only becomes visible to those who use a complexity approach: Every road user reacts differently to the opening or closing of a road. That reaction can be to move the ride to another time, to use a different road, to ride with someone else, to use public transport or to cancel the ride.

Carlos Gershenson, a Mexican computer specialist, has examined traffic behavior from a complexity approach and he concludes that self-regulation is the best way to tackle congestion and to maximize the capacity of roads. If the simulated traffic changes in Chattanooga had taken place in the real world, thousands of travelers would have changed their driving behavior in a short time. They had started trying out the smart highway, and due to induced demand, congestion there would increase to old levels in no time. Someone who wants to make the effect of traffic measures visible with a digital twin should feed it with results of research into the induced demand effect, instead of just manipulating historical traffic data.

The value of digital twins

Digital twins prove their worth when simulating physical systems, i.e. processes with a parametric progression. This concerns, for example, the operation of a machine, or in an urban context, the relationship between the amount of UV light, the temperature, the wind (speed) and the number of trees per unit area. In Singapore, for example, digital twins are being used to investigate how heat islands arise in the city and how their effect can be reduced. Schiphol Airporthas a digital twin that shows all moving parts at the airport, such as roller conveyors and stairs. This enables technicians to get to work immediately in the event of a malfunction. It is impossible to say in advance whether the costs of building such a model outweigh the benefits. Digital twins often develop from small to large, driven by proven needs.

Boston also developed a digital twin of part of the city in 2017, with technical support from Esri. A limited number of processes have been merged into a virtual 3D model. One is the shadowing caused by the height of buildings. One of the much-loved green spaces in the city is the Boston Common. For decades, it has been possible to limit the development of high-rise buildings along the edges of the park and thus to limit shade. Time and again, project developers came up with new proposals for high-rise buildings. With the digital twin, the effect of the shadowing of these buildings can be simulated in different weather conditions and in different seasons (see title image). The digital twin can be consulted online, so that everyone can view these and other effects of urban planning interventions at home.

Questions in advance

Three questions precede the construction of a digital twin. In the first place, what the user wants to achieve with it, then which processes will be involved and thirdly, which knowledge is available of these processes and their impact. Chris Andrews, an urban planner working on the ESRI ArcGIS platform, emphasizes the need to limit the number of elements in a digital twin and to pre-calculate the relationship between them: To help limit complexity, the number of systems modeled in a digital twin should likely be focused on the problems the twin will be used to solve.

Both the example of traffic forecasts in Chattanooga, the formation of heat islands in Singapore and the shadowing of the Boston Common show that raw data is insufficient to feed a digital twin. Instead, data are used that are the result of scientific research, after the researcher has decided whether a systems approach or a complexity approach is appropriate. In the words of Nigel Jacob, former Chief Technology Officer in Boston: For many years now, we’ve been talking about the need to become data-driven… But there’s a step beyond that. We need to make the transition to being science-driven in …… It’s not enough to be data mining to look for patterns. We need to understand root causes of issues and develop policies to address these issues.

Digital twins are valuable tools. But if they are fed with raw data, they provide at best insight into statistical connections and every scientist knows how dangerous it is to draw conclusions from that: Trash in, trash out.