AI Smart Cities Updated

AI Development:

To function, Smart City technologies require the processing of enormous quantities of data, or “Big Data”. Big Data has been described in terms of the three “Vs” as “high-volume, high-velocity and/or high-variety information assets” which means massive datasets, processed very quickly (through the use of algorithms) and the use of different data sources, including combining different datasets.
Big Data and artificial intelligence (AI) are interlinked. AI refers to various methods “for using a non-human system to learn from experience and imitate human intelligent behavior”. AI can efficiently sift through large quantities of Big Data to generate data predictions and cost-effective solutions to fuel Smart City technologies.
The way this works depends on whether the AI is supervised or unsupervised. In supervised learning, datasets and target values are created to train AI networks to find specific solutions in the collected raw data. The AI will then carry out programmed tasks and actions, whilst exploring new opportunities and possibilities that may provide better outcomes than current solutions. In unsupervised learning, non-labelled and non-classified datasets are used to train and ask questions of AI networks, which will then find latent characteristics and hidden patterns in the data.
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Potential use cases of AI:

1. Public transit: Cities with vast transit infrastructure and systems can benefit from applications that harmonize the experience of its riders. Passengers of trains, buses and cars can provide real-time information through their mobile apps to communicate delays, breakdowns and less congested routes. This may, in turn, encourage other commuters to alter their choice of travel routes, and free up future congestion. Collecting and analyzing public transit usage data can also help cities make more informed decisions when modifying public transport routes and timings, and allocate more accurate infrastructure budgets. For example, Dubai has completed a number of Smart City projects, one of which monitored the condition of bus drivers. This monitoring contributed to a 65% reduction in accidents caused by exhaustion and fatigue.
2. Public safety: The same networks of sensors and cameras can be used to save lives and lower crime. Traffic lights and congestion data can be used by emergency services to get to their destinations quicker and more safely. Cities can gather data on accidents or choose other factors to measure in order to develop predictive and preventative measures for the future.
3. Building automation systems: Sensors can be placed in strategic building locations that will help to gather information on energy usage and predict consumer behavior. For example, store owners and retailers can use sensors to track the peak times that individuals enter and use the stores, as well as towards which areas the public gravitates. Through the use of AI, the data generated can help to produce consistent predictions and track daily, weekly and seasonal differences.
4. Power grids: AI and Smart Cities have the potential to enhance the safety of power grids and improve performance management. Smart grids (power networks, such as generation plants, that are embedded with computer technology) can make smart meter readings of large quantities of data to assess and predict demand response and load clustering. Prediction models can be set up on these grids to forecast the price and demand for energy for specific periodic intervals. Research conducted has found that these models can surpass existing methods in terms of accuracy of price and load forecasting.