In a recent interview, Professor Elena Simperl, director of the QROWD project, outlined the objectives of the EU programme. She explained that, ultimately, the project will make transport in its various forms smarter. This will be done through improving the quality of available services that different transport operators offer to citizens, having better tools and IT service providers to support these processes, and helping those who define and implement policy to have a more informed view on what is needed.
However, the development of smart transport and a seamless city-wide network will only be an achievable goal if IT infrastructure, as well as other characteristics of smart cities, are put at the forefront of development. To support smart transport we must first take a look at the five main challenges of IT and the Internet of Things (IoT).
Optimisation is the key to maximising efficiency in smart cities. In such settlements huge volumes of big data are created on a daily basis, and these inform organisations and governments about a wealth of different aspects – from the small to the large. In order for big data to be used effectively to improve smart cities, IT optimisation is vital to storing, analysing, and prioritising data on a scale that humans simply cannot do manually.
IT integration is needed to consolidate many smart city systems to work together. Due to large and complex nature of smart cities, they require a multitude of different systems and management protocols that are tailored for different services. This can range from public transport systems to AI controlled air-flow filtration systems, each requiring unique technology and producing a variety of data outputs. The challenge then is to integrate these systems to ensure easy sharing and understanding of the city as a whole.
In order to make improvements based on the data generated by smart cities, we must first be able to analyse the outputs of a system. This is a significant challenge due to the scale and volume of data produced, as well as the type of analysis that is needed: data alone is of little value if it cannot be used to inform decisions effectively. Using advanced analytics offered by IT technology and software is therefore vital to smart city progression.
Being able to predict and mitigate the needs of a smart city is essential for its sustainability. Prediction through simulations that use big data produced by smart cities is one of main ways to achieve this. Using predictive analytics to understand the bigger picture, as well as the potential interactions between systems, is a valuable way to ensure that smart cities are functioning well and improving the lives of citizens.
The final challenge for smart cities is that of smart mobility – one which the QROWD project contributes a solution to. This is a tool to develop resource efficient transport on a city-wide scale that is both affordable and environmentally friendly. The technology and infrastructure to run smart transport and systems seek to allow easy transport for citizens, and to save time and money. Many smart transport systems are developed and employed globally, with a host of other cities beginning to invest in the scheme in the near future.