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Understanding mobility phenomena requires a multidisciplinary approach. This month, we wanted to explore one of our themes\u2014the impact of big data<\/b> on urban mobility management\u2014from a new perspective: that of economics<\/b>. Through a dialogue with Prof. Alberto Iozzi (University of Tor Vergata) and Prof. Federico Boffa and Prof. Eugenio Levi (Free University of Bolzano), we asked about the current prospects and positions of economics in this field, exploring its potential, risks, and examples of application.<\/p>\n
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From an economic point of view, big data has had a huge impact on the service sector. On the one hand, we have witnessed the emergence of a veritable big data market<\/b> and the unprecedented growth of major players in this field: \u201cThe prime example is obviously Google<\/b>,\u201d says Iozzi, \u201cwhich bases its fortune on the collection and use of big data and which, over time, has given rise to new challenges<\/b> concerning antitrust, restriction of competition, and privacy issues.\u201d<\/p>\n
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On the other hand, the advent of big data has led to a profound change in the services offered: as Prof. Iozzi states, “this type of data has been of great interest from the point of view of opening up new markets<\/b>: the use of big data has made it possible to create new services<\/b> in many areas, as well as having enormous implications in the field of statistics,<\/b> for example through the use of very high-frequency data.” Just think of the development of new sharing mobility<\/b> services, as well as navigation systems<\/b>. “Private individuals have been able to enter these markets and provide services that have changed our habits<\/b>, including in terms of mobility, changing the way we move around in urban contexts. Take navigation systems, for example: visiting a city we don’t know is now the easiest thing in the world. We rent a car, set up the navigation system, and this allows us to move anywhere in a city we don’t know, something that was absolutely unthinkable just a few years ago.”<\/p>\n
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According to Prof. Iozzi, the public sector is certainly not yet recognizing the potential of big data, and even in academia, we have not been able to anticipate the phenomenon, adopting a reactive<\/b> rather than proactive<\/b> approach to understanding it: “What we have been talking about is the private use of information. It seems to me, however, that the economy has somewhat failed to imagine<\/b> the possible developments in this sector, focusing too much on analyzing what already exists and not on the potential of these tools from the point of view of public administration<\/b>.”<\/p>\n
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The focus has therefore been on studying new services (e.g., sharing mobility) without devoting sufficient energy to exploring the possibilities <\/b>that big data can open up in many areas of public interest: \u201cFor example, healthcare<\/b>, for the improvement of health services. Or the use of big data for the management of fleets of autonomous cars <\/b>and the possible developments in urban mobility, since they will soon represent a significant part of it.\u201d<\/p>\n
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What is still lacking, especially in the public sector, despite recent progress, are skills<\/b>: \u201cThere are attempts by the public to access data from large companies such as Google, Waze, or Octo Telematics. However, the ability to put together<\/b> the different pieces of this puzzle is still lacking.\u201d The public authorities are therefore still in an immature situation with regard to solutions for accessing data and using these technologies: “In many cities, real-time traffic information is provided via traffic signs, which do not fully exploit today’s technological potential; the same is true for traffic measurement methods. On the one hand, we need to strengthen technical capacity<\/b>, and on the other, we need reasonable public policy objectives<\/b>.”<\/p>\n
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Speaking of ambitions in the field of public policy, a question arises: to what extent can these new technologies<\/b> contribute to the improvement (or deterioration) of the urban environment? Returning to the example mentioned earlier by Prof. Iozzi: \u201cBefore the advent of these technologies, if I went to a new city and had to use public transport to get around, now I can easily rent a car and get around easily thanks to navigation systems. So what is the overall effect<\/b>? Increased demand or improved individual behavior?\u201d<\/p>\n
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To answer this question, we must first introduce the economic concept of \u201csocial optimum<\/b>,\u201d an economic version of the engineering concept of \u201csystem optimum\u201d (words are important<\/i>, cit.). The goal of social optimum is to find an optimal distribution of resources and policies that maximizes the overall well-being of a system or society (as opposed to the pursuit of individual optimum for each person). This concept takes into account the different needs and preferences of people, seeking to balance the benefits for the greatest number of individuals possible.<\/p>\n
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How can big data help pursue social optimum? \u201cThere are many pieces of the puzzle that are necessary. First and foremost, the ability to manage big data<\/b>. Secondly, the planning capacity<\/b> of public authorities. Finally, the systematization of the two, which is far from easy.\u201d<\/p>\n
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However, there is already evidence<\/b> of systems moving in this direction on the private side. Iozzi continues: \u201cUber uses so-called surge pricing<\/i><\/b>, which is a surcharge that users pay when demand is particularly high in a particular geographical area and at a particular time, and which incentivizes drivers to meet the peak in demand. How is information about this peak in demand obtained? Obviously by using big data<\/b> in real time.\u201d<\/p>\n
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The role of the various applications of this concept, in fact, depends very much on technology. One example is the management of autonomous cars<\/b>, with which we already have experience (albeit in limited situations): “It is one thing to manage a fleet of Uber cars driven by drivers<\/b>, who can determine the different routes to follow regardless of what you suggest to them, and which would be functional to the pursuit of social optimization. It is quite another to manage a fleet of autonomous cars, which you have the power to direct<\/b> according to the route you consider best for system optimization.\u201c<\/p>\n
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This is precisely where the crux of the matter lies: \u201dAssigning routes according to the social optimum means that, in order to achieve the best result in terms of the community, some individuals may suffer a deterioration<\/b>. In the case of an autonomous car, of course, behavior can be \u2018guided\u2019 towards the social optimum. But in the case of individuals, since they have the freedom to deviate from the suggested route, it is necessary to provide the right incentives<\/b> and the right motivations<\/b>. It is in this field that communication<\/b> becomes of fundamental importance.”<\/p>\n
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Once again, when it comes to data and mobility governance, the issue of communication<\/b> takes center stage. As Prof. Iozzi pointed out, it is not just a question of monetary incentives: \u201cthese new technologies and complex system management capabilities will necessarily have to be accompanied by a series of policies<\/b> that encourage individuals to adhere to these projects, even accepting what could be minor deteriorations from a strictly utilitarian-individual point of view.\u201d<\/p>\n
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And this is where we enter the field of behavioral economics<\/b>: \u201cIn our team, we are trying to study the effects of the spread of navigation systems on congestion, based on actual data. At the same time, we are conducting experimental analyses to investigate people’s predisposition<\/b> to pursue the optimal system, verifying which subjects are more inclined to accept this type of proposal. For example, based on income, risk appetite, or the type of information available.\u201d<\/p>\n
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Thanks to activities carried out in the laboratory, it is possible to analyze this information and verify the possibility of influencing individuals’ behavior with so-called \u201cgentle nudges\u201d (nudging). Nudging<\/b> consists of developing techniques to encourage better individual choices, from a social point of view, through small suggestions or changes in the environment in which people make decisions (e.g., displaying healthier foods at eye level in a supermarket to encourage healthier food choices). It is important to emphasize that nudging does not force<\/b> people to do something against their will, but rather creates an environment that facilitates the desired choice, thus respecting individual freedom of choice.<\/p>\n
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Let’s delve into this topic with Dr. Eugenio Levi, a researcher in behavioral economics, who tells us about some tools that public decision-makers<\/b> can choose to use to present choices to people and guide them toward the social optimum, especially in the field of transportation: “If we enter into the topic of \u2018centralized\u2019 management of city congestion through digital tools, there are two issues. One is nudging, i.e., the way in which choices are presented<\/b> to people. Certainly, one way to convince people to participate in these projects is to make it clear that they serve to increase overall well-being.”<\/p>\n
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As Levi explains, empirical evidence shows that emphasizing the common identity <\/b>of project participants, thereby increasing their sense of belonging<\/b> to their city, helps to overshadow more individualistic<\/b> motivations (in this case, for example, choosing the fastest route even if it is the one that would increase traffic overall).<\/p>\n
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Levi continues: “Secondly, we also know from various behavioral economics studies that people are very sensitive to the type of information they are given about the behavior of others<\/b>. For example, people tend to be more motivated to make certain choices if they know that they are made by the majority of other people. Communicating that the majority of people are participating in the project can therefore be an effective incentive<\/b> to follow the suggested routes. Furthermore, there is a lot of experimental evidence that when people vote<\/b> for a certain choice, this increases their actual adherence to the choice made collectively. In other words, collectively chosen<\/b> rules lead people to adhere to them more spontaneously.”<\/p>\n
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The use of nudging techniques in combination with big data can provoke negative reactions, particularly in relation to privacy<\/b> and freedom of choice<\/b>. As Prof. Boffa states, \u201cit may be somewhat reminiscent of George Orwell,\u201d and it would certainly not be the first time that the adjective \u2018Orwellian\u2019 has been used to describe the current applications of big data. Boffa continues: \u201cHowever, the alternative to guiding individual choices for the good of the community through nudging is to force<\/b> those choices, and in this respect, nudging is preferable.\u201d<\/p>\n
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Levi concludes: “Another controversial element of using big data in combination with nudges is a certain degree of paternalism<\/b> inherent in this approach. But when it comes to using big data for measures aimed purely at raising the quality of public services without requiring corresponding individual choices on the part of users, this problem should not arise. For example, to improve the management of public transport routes or traffic light timing, the use of big data is sufficient, regardless of any active behavior on the part of users. The extent to which public decision-makers can exploit big data to improve the quality of public services should not be a controversial issue.”<\/p>\n
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