Big Data is an issue that is quite well-known and debated. Until a few years ago, many did not know what they were and, even though the level of awareness and understanding of this phenomenon has now increased, there are still many people who do not know exactly what they are.
When we speak of Big Data we are referring to an extensive collection in terms of volume, speed, virality, vulnerability and variety of data which, seen from the outside, may seem like a disordered collection but which, when analyzed with specific methods, manage to provide information of value. Like any revolution that invests the sphere of technological innovation, the focus is often on the tools and their capabilities, losing sight, in my opinion, of what is the true essence of big data: the birth of a new mentality, perspective and paradigm for the management of complex phenomena involving the human sphere, relations and space.
Some figures on big data
The figures that accompany big data are Impressive: according to a study by the Luiss University of Rome, the estimated value for the data of our digital lives is around a trillion euro for 44 trillion gigabytes of data expected for 2020. All internet-based services have benefited from this new technology: it is estimated, in fact, that the analysis of Big Data carried out by Amazon generates around 30% of the company’s income.
Which are the sectors in which big data are useful?
The sectors that have benefited from the introduction of big data are mainly the health care, education and mobility and transport systems. In health, for example, around 150 thousand billion Gb are produced by the human body and 25 thousand those that hospitals will collect by 2020. What can they be used for? In the first place, for better disease prevention which comes from research that can benefit from analysis of much information.
Big data and public transport
Big Data are having a major impact on the morphology and urban planning of the city; so much so that they are already used, for example, to predict the needs of public transport systems, by orienting investments and mobility policies within metropoles.
Big data can be generated everywhere and using any digital device. In fact, they can be produced by mobile phones, social media, sensors, transactional systems, automobiles, industrial machines, PCs, satellites and cameras for monitoring traffic. The capillary diffusion of wireless technologies and the entire network infrastructure allow the detection and collection of large amounts of spatio-temporal data that can be used to understand innovative interpretative patterns and models which, in the specific field of mobility, can guide urban planning, sustainable mobility and transport engineering. Having the ability to access data from different sources, collect them, aggregate them, process them and analyze them, thus means having the ability to make decisions and give answers to problems in near real time and reliably.
The case of Roma Servizi per la Mobilità (Rome’s Mobility Services)
Through Roma Servizi per la Mobilità, Rome has set up a mobile information platform that provides assistance services for the movement of citizens, tourists and city users. Through heterogeneous monitoring systems, the Agency “photographs” and stores the state of city traffic state and the precise position of local public transport vehicles. From the information point of view, data are provided to users through several channels, including:
- line search: the service makes it possible to search for local public transport lines and show static information (routes, stops, maps) and dynamic information (measured travel times, vehicle location, waiting times at bus stops);
- route search: what is called in technical terms dynamic multimodal route planner. Through knowledge of the actual state of the network, the service suggests the best route for reaching the destination moment by moment. The modes of transport considered are: public transport (urban buses and trams, subway, suburban and regional railways), private transport and sustainable mobility (bike and ride, park and ride, car sharing, car pooling);
- News: the service provides information on public and private mobility.
The data are used both for prediction of traffic flows on major city streets and for near real-time adjustment of the traffic signal network. The main dataset products are distributed as Open Data on the Agency’s portal, with a Creative Commons license that allows sharing and reuse. The source code for most of the mobile information platform is also published under the GNU General Public License v2 license.
The aforementioned case is just one individual case of success that makes one understand how data can be effectively used within an urban space. But the potential is endless, especially if we think about the possibility of creating an integrated approach to intermodal transport (park and ride, bike and car sharing, electric mobility, proactive car pooling, etc.) and the possibility of creating synthetic indicators for evaluating and improving urban mobility.
The hope for the near future is that big data will allow us to manage and improve urban transport policies and thus improve the quality of life in our cities.