# An ML-Based Solution in the Transformation towards a Sustainable Smart City
## 2 AI in SSC
### AI in Selected Areas of SSCs
Data management strategies, but also the sharing of data by consumers, institutions, organisations and industries, can be supported by edge clouds, thus protecting privacy and improving performance. Many solutions (e.g., mobility systems) usually operate based on the agents'/vehicles' self-interest, as part of integration with solutions for autonomous drivers [3]. Traffic simulation tools can be used to determine the duration of transport use. Pedestrian traffic routes pose a challenge; they can also be used for autonomous vehicles delivering parcels to shops, service premises and houses or apartments. Found in areas intended for pedestrian traffic stakeholders in such a logistics process are primarily courier service providers and their customers, but also SSC traffic and logistics managers--fragmentation of the \"last mile\" of transport services in this way will shorten the time in which the shipment reaches the recipient (also, regardless of the time, in 24 h) [3]. Automated diagnostics and support in apartments, nursing homes, rehabilitation centres, hospitals and sanatoriums require the use of edge computing, fog and clouds for rapid processing and response. It is also the only alternative in the absence of caregivers, physiotherapists or doctors, and perhaps also psychologists and family. The complexity of such a problem requires hybrid solutions combining mathematical models with ML [3; 12]. SSCs can promote so-called active transport, such as walking or cycling. In turn, such appropriately measured activity may contribute to increasing the overall level of physical activity and changing activity habits, and, over time, dietary habits, work-private life balance or preferred forms of tourism. Apps for smartphones, tablets, smart TVs and vehicle infotainment can also promote this behavioural change. The aggregate benefits from such seemingly minor changes within the SSC may be significant and, in the long run, inevitable, and should therefore be monitored and stimulated to avoid going in the wrong direction [47; 48; 49; 50; 51; 52; 53; 54; 55]. It is worth looking for cheap solutions so that the application has the potential to affect many copies on a mass scale. The impact of the time it takes to get to work and school (on foot or by various means of transport) on the quality of life is large: the longer employees' commute time, the lower their satisfaction with work, life and health. It is worth knowing how many vehicles, appropriately equipped with sensors, would be needed to properly scan the SSC for a specific account. Publicly available data on the movement of individual travellers is contained both in their smartphones and in traffic lights and city monitoring. This allows, for example, the simplification of location based on movement trajectories. Even controlling pedestrian and vehicle traffic and effectively managing congestion and delays help reduce travel times and save many valuable resources. Basing such a system on three basic elements: vehicle, infrastructure and events, enables taking all scenarios and possible problems of the transport system into account. (including the machine learning-based DBSCAN clustering method for anomaly detection). As people migrate between SSCs or from rural to urban environments, cities must optimise their services (services, logistics and transport) to best respond to changing demand [3]. ML can solve some of the problems associated with decentralised computing, enhancing cyber security and data privacy, but it needs further development. This is implemented, among other things, as part of a health impact assessment (HIA), health economics, the impact of health recommendations on urban policies, the burden of disease/injury at the city level and the involvement of stakeholders (residents, city authorities, representatives of associations of medical specialists and patients) [45; 46; 47; 48].