{"id":3990,"date":"2024-11-20T18:32:50","date_gmt":"2024-11-20T17:32:50","guid":{"rendered":"https:\/\/datamobility.it\/magazine\/understanding-numbers-to-build-the-future\/"},"modified":"2025-12-10T12:35:33","modified_gmt":"2025-12-10T11:35:33","slug":"understanding-numbers-to-build-the-future","status":"publish","type":"post","link":"https:\/\/datamobility.it\/en\/magazine\/understanding-numbers-to-build-the-future\/","title":{"rendered":"Understanding numbers to build the future<\/strong>
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GO-Mobility<\/strong><\/h3>\n

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Understanding numbers to build the future<\/h1>\n

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Interview to Daniele Mancuso<\/h3>\n

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What does an increase in car use today tell us? And why could this be beneficial for public transport in the future? Supporting institutions and organizations in defining the mobility of the future is the task of GO-Mobility, which we carry out through an in-depth immersion in transport data. In this interview with Daniele Mancuso in \u201cIl Sussidiario.net,\u201d we delve into these questions and possible answers thanks to an integrated reading of the various sources available\u2014especially big data from FCD and telephone data. Let’s begin!
The interview
[Il Sussidiario] What does big data say about mobility? Does road transport \u2013 particularly private cars \u2013 still reign supreme?
[Daniele Mancuso] It is certainly the most prevalent form of transport and is expanding at the expense of public transport. But this is not always the case: in cities, public transport by tram and metro has recovered compared to the Covid period. On the other hand, we have lost commuters who used to opt for public transport for medium- to long-distance journeys but now, thanks to remote working and the replacement of many face-to-face meetings with online calls, find it more convenient to use private cars.
But why is car traffic increasing if there are fewer journeys to workplaces?
If you only have to go to the office two or three times a week, a car is often more convenient and comfortable than public transport. Above all, working from home does not mean not going out. On the contrary, cars are used more often for activities that are structurally more difficult to access by public transport, such as leisure, errands, or shopping. People’s habits and needs have changed, and public transport is therefore struggling to recover.
What kind of innovative solutions have you helped to implement thanks to the processing of travel data?
First of all, as an engineering company involved in mobility planning, we believe it is essential to break down the wall of opinion as much as possible. When it comes to mobility, everyone thinks they are an expert, if only because they travel by car or use public transport. In reality, the decisions on which investments to make to manage the mobility of people and goods are very complex and have always suffered from two major shortcomings. The first concerns ex ante information, i.e., structured, continuous, and complete data on mobility needs. The second concerns ex post information, i.e., monitoring what happens as a result of these decisions: once a decision has been made, the administration in charge acts by defending it a priori as the most correct decision.
What is the problem in this second case?
That it is more a defense than transparent information. Participatory processes, which have been gaining ground for some years now in public decision-making, involve building consensus and involving stakeholders from the outset. These processes are based on reliable and up-to-date data, which is collected and analyzed in a shared manner in order to reconstruct and understand reality, debunking urban legends and false myths.
What are the myths to be debunked?
Let me give you an example. Starting with slightly longer journeys, the approach to building public transport networks has always been to satisfy even small communities or individual users who may have exerted their influence in small towns. However, in addition to being based on the erroneous law of who shouts the loudest, this also results in very long and expensive services, as they require a large number of vehicles and staff. It would be more effective to build a hierarchical transport network, based on lines with different frequencies and capacities across the territory, based on an in-depth study of travel and mobility needs.
This means, for example, serving routes with higher traffic with high-frequency direct lines, while in less busy areas, services that involve minor inconveniences such as line changes (rather than a direct line that only runs once a day). We need well-designed networks that are much more resilient to temporary closures or problems that may occur along the route. If there are long and complex networks, a problem in the northwestern part of the city can have repercussions as far as the southeast.
In what other ways does the approach need to change?
It is thought that the task of a transport company is to run vehicles and therefore produce kilometers, but in reality its customer is not the kilometer, but the person, who should be placed at the center with their needs. It should be responsible for planning. Instead, service contract payment systems are essentially based on kilometers, not passengers.
What are some good examples that you have helped to implement?
If we are talking about the transmission of a data culture in the field of transport, the first example is Trenord, because it was the first company in Italy to invest in a systematic approach to reading mobility data: the so-called data-driven approach. It was the first company to follow us and is internalizing this type of approach, which is not easy to achieve, into its DNA. For the rest, today we are seeing greater openness regarding the decarbonization of transport: there are important international players who are adopting the data-driven approach to provide a scientific basis for both their carbon footprint and the projects designed to reduce and eliminate the emissions of the transport systems that impact their activities.
What advice can you give to companies in this regard?
Big data allows us to take a series of very detailed photographs of the impact generated in terms of emissions. The aim is also to understand the effects of third parties: the world of emissions is divided into two blocks, one over which the company has direct control and one in which it competes with other players to reduce emissions. The second is the most challenging: there is a need to bring everyone to the table, from the European Commission with the Green Deal to the individual operator who wants to reduce emissions. Energy, industrial policy, and planning choices influence traffic and motorization in individual countries.
Is cooperation between different actors at different levels necessary?
To achieve this, we need awareness and knowledge of our surroundings, and big data can contribute to this. We reconstruct diagnostic tools and simulation models that allow us to test different scenarios, and we objectively evaluate the consequences of the various possible choices.
Do you enable companies to understand their emission levels and how they could be reduced?
We help companies understand their emissions: how much they currently emit and how much they can reduce their emissions in the coming years based on the commitments of others to contribute to this reduction. To go to a trade fair or the airport, there is currently a pronounced use of cars, and even if there is a rail or subway connection, if they are powered by fossil fuels, passengers using public transport also emit CO2. The emissions generated by passengers therefore vary according to the energy source used to power these vehicles. It all depends on the country’s energy choices, but also on company bonuses to encourage the use of railways. Data in this context is a means by which we can compare ourselves objectively.
Is artificial intelligence already helping you in all this?
Yes. AI makes an excellent contribution to compensating for the lack of specific workforce, but it does not replace people. We have developed small virtual assistants that do not replace our consulting services, but provide assistance to institutions and companies in carrying out basic activities independently, allowing for a better understanding of the information in our possession. It is a highly appreciated system that reduces execution time in many situations. The sector has a structural shortage of resources.
In concrete terms, how does AI contribute?
Public transport agencies find it difficult to understand exactly whether transport operators are complying with the operating schedule, whether there are changes in the service, or even just how many passengers are boarding and how many tickets are being sold. Ex-post analyses are carried out on this, often by the transport companies themselves. Agencies therefore have to rely on reports that arrive up to a year late. With more effective tools, supported by a virtual assistant, they can automatically obtain what, until recently, they would have had to wait a long time for. To achieve this, however, we need companies that are convinced they want to embark on this path and are committed to continuing in this direction.
What kind of mobility can we imagine for the future, based on what we know now?
We are in a period of transition. In my opinion, the system will lean towards even greater use of the car. Public transport is not undergoing the same transformation as the automotive sector. What will come next will come from there. The change in mobility has been driven by people’s new needs, but it will soon be completely triggered by the technological revolution in cars. Public transport, as it is currently conceived, is not able to compete. However, with the exception of medium- to long-distance trains and subways, I expect the sector to move closer to something like private car sharing in the future. We will move towards an on-demand system, which offers opportunities much more similar to those that have so far been the prerogative of the car.<\/p>\n

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Read the interview on Il Sussidiario.net<\/a><\/p>\n

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