AI Smart Cities

AI Intro:

A “Smart City” is an urban area which relies on information and communication technologies to build economic growth, improve quality of life and underpin governance structures. For example, a municipal authority could interconnect its transport and energy grid systems, build sensor-equipped energy-efficient buildings and develop communications that enable better monitoring of and access to healthcare, emergency and other public services.
The McKinsey study suggested that there are three layers that intertwine to make a Smart City function. Firstly, the technological base consists of smartphones and sensor-equipped devices producing data and connecting to high-speed communication networks. Secondly, computers process the data to deliver workable solutions for specific problems. Thirdly, the general public interacts with these technologies – and all of the applications of Smart City technologies depend on individuals simultaneously using them and providing data to generate predictions.
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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. Intelligent Security Cameras: Computer vision to enhance security cameras is a pretty straightforward application of AI. Having a video where a crime is recorded is great, but unless someone knows something occurred, there is simply no reason to review the footage until that footage is eventually lost. A surveillance system supported by a robust AI looking for patterns of criminal behavior is the equivalent to a team of detectives that never sleep analyzing all video in real time. AI-enhanced security cameras can be used in schools and businesses to cut the response time whenever action needs to be taken. For example, if the person that needs to be detected is a “white male wearing a blue shirt,” the AI can differentiate between people entering an area who corresponds to the description and send an alert in real time. His photos and video can also be uploaded directly to local first responders, who can find segments of videos which may contain him based on keywords (white, male, and blue shirt, in the above example) instead of having to search through hours of footage. In Japan, an AI-powered security camera is so smart that it can estimate the poses of a suspicious person who is likely to commit a shoplifting crime.
4. 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.
5. 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.
6. Computer Vision and Parking Systems: In the parking space, AI has been used by companies such as Pixevia to integrate computer vision and advanced algorithms into a smart ecosystem. By combining many advanced functions such as license plate recognition and pixel detection, off-the-shelf cameras can provide real-time information on space availability to customers and parking operators, and automatically enforce parking payment and duration. Advanced algorithms can provide precise car position estimation, and predict parking usage during certain times of the day or night. Eventually, this technology will be a perfect match for the upcoming autonomous vehicles which will be able to “speak” directly with parking lots and garages.