Real-time System for Short- and Long-Term Prediction of Vehicle Flow

Nowadays, traffic management and sustainable mobility are becoming one of the central topics for intelligent transportation systems (ITS). Thanks to the today's technologies, it is possible to collect real-time data to monitor the traffic situation in some specific areas. An important challenge in ITS is the ability to predict road traffic variables. The short-term predictions of traffic aspects are a complex nonlinear task that has been the subject of many research efforts in the past few decades. Accessing to precise traffic flow data is mandatory for a large number of applications which have to guarantee high level of services such as: traffic flow reconstruction, which in turn is used to perform what-if analysis, conditioned routing, etc. They have to be reliable and precise for sending rescue teams and fire brigades. This paper proposes a solution for a short- and long-term traffic flow prediction estimation by using and comparing a number of machine learning approaches. The solution has been developed in the context of Sii-Mobility smart city mobility and transport national project and it is in use in other EC projects and solution such as Snap4City PCP EC and TRAFAIR CEF, but also for REPLICATE H2020 SCC1 and control room in Florence area.