{"id":3873,"date":"2025-11-26T10:18:30","date_gmt":"2025-11-26T09:18:30","guid":{"rendered":"https:\/\/datamobility.it\/?p=3873"},"modified":"2025-12-10T12:06:12","modified_gmt":"2025-12-10T11:06:12","slug":"make-tpl-great-again-2","status":"publish","type":"post","link":"https:\/\/datamobility.it\/en\/magazine\/make-tpl-great-again-2\/","title":{"rendered":"Join the Data Revolution<\/strong>
"},"content":{"rendered":"

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From the speech of Armando Carten\u00ec | Osservatorio Sunrise<\/h3>\n

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Join the Data Revolution<\/strong><\/h1>\n

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Ma preparati a conoscerne benefici e rischi<\/span><\/h3>\n

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For decades, transportation systems were studied using static<\/b> surveys that produced little more than partial snapshots of demand, at very long intervals. Today, however, the availability of continuous streams of real-time data marks a radical change: from traditional planning to adaptive planning<\/b>, capable of dynamically monitoring, predicting, and correcting services. But what risks does this revolution entail? Professor Carten\u00ec discussed this with us, reflecting on the benefits and challenges of the big data revolution<\/b> in the field of mobility planning.<\/p>\n

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Join the Data Revolution<\/strong><\/b><\/h3>\n

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When you consider the speed and \u201carrogance\u201d with which the rules are being subverted in the field of mobility data analysis, it is no exaggeration to speak of a true revolution<\/b>. For decades, mobility has been studied and planned on the basis of surveys repeated years apart, sometimes even decades, possibly refined with interviews or one-off traffic counts. The result was little more than a static snapshot<\/b> of mobility habits. It was unthinkable to talk about seasonality, off-peak hours, or subcategories of users<\/b> other than those typical of workers and students.<\/p>\n

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Today, the situation has changed radically: thanks to the increasing availability of data, we have moved from episodic sampling to monitoring platforms<\/b>, i.e., systems that collect data continuously. Big data<\/b>, with its two defining characteristics (Volume and Velocity), allows mobility systems and their operation to be monitored in real time<\/b>, opening the door to adaptive planning<\/b> that was unimaginable just a few years ago. For example, it is possible to plan an offer that meets real demand <\/b>and is corrected on the basis of ex-post evaluations<\/b>, rather than quantitative considerations based on limited data.<\/p>\n

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But that’s not all: today, it is possible to start managing early warning<\/b> systems that integrate analysis and monitoring components with forecasting<\/b> models and decision support, command, and control systems, which were impossible to manage a few years ago with the tools available.<\/p>\n

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\u201cToday we are inundated with an unprecedented amount of data: the data sources we can draw on to read mobility and develop decision support systems are unprecedented. We analysts are intoxicated by the amount of data available<\/i>.\u201d<\/p>\n

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But like all intoxicating things, it is not a risk-free process. We are facing what Prof. Carten\u00ec defines as a veritable flood of data<\/b>, and it is very important not to be overwhelmed by it, but to develop tools to manage it. How?<\/p>\n

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\u201cOn the one hand, we can continue to do the work of planners and analysts as we did in the past, but with different tools. Or we can \u2018attack\u2019 this deluge and structurally change the way we capture, organize, analyze, and validate this data to increase its value.\u201d<\/p>\n

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Big data, big opportunities<\/strong><\/b><\/h3>\n

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As Carten\u00ec explains, we can distinguish between two generations of data:<\/p>\n

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