7. Data Needs and Utilsation

This chapter deals with the issues regarding the utilisation of AI in the management and analysis of big data for climate friendly mobility applications. After a brief but concise description of the modern collection methods for such data it goes through the main AI techniques for data processing to ensure data quality. The main advantage of using AI for this sort of quality assurance exercise is its ability to train algorithmic models so that they can recognize trends and hidden issues and extract useful information with very short processing times hundreds or even thousands of times faster than traditional data processing methods. The text explains how machine learning or deep learning algorithms and other AI based technology can automate complex data analysis tasks, excel at identifying complex patterns and relationships from big data, discover valuable information hidden in these data and increase its accuracy and reliability. Consequently, there is a concise reference to the many AI tools and software libraries that can be used for such big data analytics. A few indicative examples of existing applications of big data in the transport and mobility sector follow as an indication of the use of AI techniques for this purpose in practice. With the help of AI, big data gets a number of quality attributes which include Accuracy, Availability, Integrity, Consistency, Completeness, and Precision. All these are explained and discussed in length. Finally, this chapter duels on the issue of energy consumption that is necessary for AI-assisted big data analytics and calls this situation “a mixed blessing”. This characterization is justified by the fact that whereas the use and utilisation of AI for big data analysis and handling has many advantages and can be a beneficial transformational force, it necessitates large quantities of electricity which if not generated by “clean” renewable sources it can generate more carbon emissions than saved from transport operations by using AI.