Today an unprecedented amount of data coming from several sources, including mobile devices, sensors, tracking systems, and online platforms, characterizes our lives. The term “big data” not only refers to the quantity of data but also to the variety and speed of data generation. These data hold valuable insights that, when extracted and analyzed, facilitate informed decision-making. The 4Vs of big data - velocity, volume, variety, and value - highlight essential aspects, showcasing the rapid generation, vast quantities, diverse sources, and potential value addition of this kind of data. Big data’s pervasive impact enhances societal aspects, elevating the quality of life, service efficiency, and problem-solving capacities. However, during this transformative era, new challenges arise, including data quality, privacy, data security, cybersecurity, interoperability, the need for advanced infrastructures, and staff training. Within the transportation sector (the topic investigated in this research), applications span planning, designing, and managing systems and mobility services. Among the most common big data applications within the transport sector, there are, for example, real-time traffic monitoring, bus/freight vehicle route optimization, vehicle maintenance, road safety, and all the autonomous and connected vehicles applications, in addition to the travel demand estimation useful for a sustainable transportation planning. Emerging technologies, offering substantial big data at lower costs than traditional methods, play a pivotal role in this context. Starting from these considerations, the present study explores two recent Italian big-data applications within the transport sector starting from the database of the Italian Ministry of Infrastructure and Transport and the Ministry of Health. The first one investigates the proper national demand estimation by transport mode and territorial area of interest, while the second one correlates the diffusion of the COVID-19 pandemic with the mobility habits in the Country. The lessons learned from these case studies are: i) the large amount of mobility data is useful for estimating mobility habits as long as they are adequately treated (e.g. high professional skills are necessary) to certify the quality of the data; furthermore, also multi-source and multi-format data can significantly contribute to a better knowledge of the phenomenon, but only if they are adequately archived and processed; ii) the large amount of data made available to the different (many) operators/institutions has made possible to correlate the spread of the pandemic with the behavior of citizens; concerning transport sector, was observed that the daily mobility habits influence infections registered three weeks later and areas with higher transport accessibility are more rapidly affected by infections.
