The proposed research project is framed in the area of the Smart Cities. A Smart City is a city with the capacity to respond to the challenges posed by technological development in terms of socio-economic improvement and quality of life. Thus, it is the city itself that uses technology, especially Information and Communication Technologies (ICT), to transform its basic systems and optimize the return on investment despite the limited availability of resources.
Objective of the research The main objective of the proposed research is the development of mathematical models capable of estimating the availability of the car park complex, in order to be able to use this information to optimise its management services. Thus, our proposal will consist of the definition of a pioneering technology-based service capable of collecting, filtering, integrating and analysing static parking data (Fastprk), as well as dynamic traffic and environmental data. The latter is necessary as these two factors clearly influence parking behaviour. This will be done using procedures for merging heterogeneous data generated from different sources, as well as through predictive analysis models.
The predictive service proposed will be very useful for the public as a whole, which will be able to receive more accurate and realistic information on the future availability of the car parks, both in the short and long term. On the other hand, this information will be key for the parking system managers to define an optimal management plan for their resources. To achieve the objectives, research will focus on the area of predictive analysis that covers a wide variety of statistical techniques such as predictive learning, machine learning, and data mining.
These techniques are based on the analysis of “in-time” and historical data to make predictions about future events or other possible fictitious situations. In this way, predictive analysis aims to extract information from the data and use it to predict trends and behaviour patterns. In a Smart City, the value of predictive analytics data is to predict and prevent potential problems in order to achieve “near-zero” failure and thus optimize the use of resources. In addition, it can be integrated with existing services to optimize decision making at all levels (suppliers and consumers).
