Smart cities rely on AI-based monitoring systems to improve citizens’ quality of life. From better public safety to increased mobility, AI has a lot to offer.
Technology is needed in order to make cities work. While putting people in close proximity in the local community has certain advantages, there are also costs associated with fitting so many people and related activities into the same place. Unsurprisingly, cities inspire people to develop new technologies – once the stuff of science fiction! – that respond to the urban challenges of their day. And with the help of organizations like the Smart Cities Council, cities like San Francisco are being motivated to become smarter.
When it comes to smart city technology, cities rely on a vast, interconnected network of WiFi-enabled sensors and other devices that optimize findings from big data that translate into an increased quality of life for both residents and visitors. One of the most important components of this intelligent system? It’s ability to leverage artificial intelligence (AI) to improve the quality of life for citizens.
Smart cities generate huge amounts of data. The data that is created is of no use until and unless it is processed, which generates information in return. Artificial intelligence can then make the most sense out of this data. AI technology allows machine-to-machine interaction by processing the data and making sense out of it. For example, in a system including energy spikes, AI can learn where they usually occur and under which circumstances. This information can then be used for better management of the power grid. Likewise, AI systems also play a role in intelligent traffic management and healthcare facilities.
How does it work? According to Brookings, the amount of data created by smart cities enables the Internet of Things (IoT) to function. Essentially, IoT represents the systems that help sensors deployed across various built environment systems and equipment to speak to one another, increasing both the velocity and volume of data movement and creating new opportunities to connect physical operations. In a smart city, interconnected sensors, data management, and analytical platforms enhance the quality and operation of built environment systems.
Here, AI and machine learning come into play. The goal of machine learning is to mimic how humans might assess a given problem set using the best available data, mainly by building a layered network of small, discrete steps into larger wholes known as neural networks. As the algorithms continue to process more and more data, they learn which data better suits a given task. And with the ultimate goal of strong AI and deep learning, eventually, AI’s intellectual capabilities will be functionally equal to humans.
With the help of machine learning, many countries are already utilizing AI so that it can be deployed for city services while also monitoring streets, roads, and highways. Through the application of this technology, cities will be able to eliminate the need to place policemen on every street corner and even prevent citizens from developing respiratory problems due to the ever-present air pollution that tends to hang around cities. Whether technology is supplied by the private sector or public sector, the following are benefits across the board:
McKinsey writes that one of the aspects critical to improving quality of life is improving the daily commute. By 2025, cites that deploy smart-mobility applications may cut commuting times by 15 to 20 percent on average. This, of course, is related to variables such as the city’s density, commuting patterns, and existing transit infrastructure. Generally, cities with extensive, often-used transit systems benefit from applications that streamline the experience for riders through digital signage or mobile apps that deliver real-time information about delays. Further, installing IoT sensors on existing physical infrastructure can help crews fix problems before they turn into breakdowns and delays. Apps that ease road congestion are most effective in cities where driving cars and buses are the main forms of transport systems.
The intelligent syncing of traffic signals have the potential to reduce average commutes by more than 5 percent in developing cities where a lot of people travel by bus. Real-time navigation alerts also let drivers know about delays, helping them choose the fastest route. In the meantime, smart-parking apps point drivers directly to available spots, easing traffic and allowing for better travel overall. Further, data from IoT-connected devices with automation and artificial intelligence transform the way travel hubs like airports, bus stations, and light rail systems are managed. This new face allows Hub managers to meet the customized demands of the local travelers’ flow.
Deploying a range of applications to their maximum effect could potentially reduce fatalities like homicide, fires, and road traffic by 8 to 10 percent. Further, incidents of assault, burglary, auto theft, and robbery could be lowered by 30 to 40 percent. When it comes to crime, agencies can use data to deploy scarce resources and personnel more efficiently. For instance, real-time crime mapping utilizes statistical analysis to highlight patterns.
Predictive policing can anticipate crime and when incidents do occur, applications such as home security systems, gunshot detection, and smart surveillance can make law-enforcement response happen more rapidly. Yet, data-driven policing must be deployed in a way that avoids criminalizing specific neighborhoods or demographic groups and protects civil liberties. Further, when lives are at stake, smart systems can optimize call centers and field operations while traffic-signal preemption can give emergency vehicles a clear driving path. These types of applications could cut emergency response times by 20 to 35 percent.
Smart cities are making an effort to fight against adverse factors like the gradual rise in global warming, debris in oceans and trash in the streets, and the greenhouse effect. Energy-efficient buildings, air-quality sensors, and smart energy resources are giving cities the capability to shrink the negative effect they are having on our environment. For example, cities’ air quality sensors can help to determine and track the low quality of the air, identify pollution causes, and deliver relevant data so that appropriate actions can be taken.
Through the intersection of lighting, parking, and traffic, the underlying infrastructure of a city gets a major boost in sustainable development. Through technologies like energy-saving LED lights and photo sensors, an integrated street illumination/ surveillance/sensor solution dramatically cuts down on infrastructure and operational costs.
According to Becoming Human, smart cities’ main motive is to make inhabitants’ lives comfortable and convenient. So, it only follows that smart city infrastructure is not complete without smart governance. Smart governance implies the intelligent use of information and communications technology in order to improve decision-making through better collaboration among local government and citizens. Smart governance can use data, evidence, and other resources to improve decision-making and compliance towards citizens’ needs.
At Stefanini, we provide excellence in Smart Solutions, with a focus on the people who visit, live, and operate within smart cities. Our expertise includes Industry 4.0, Cyber Security, Cognitive, Customer Experience (CX), and more. We offer automation and AI solutions to bolster productivity as well as User Experience (UX) support. At the end of the day, innovation and digital transformation are the main pillars of smart city initiatives; we are here to help you with that process.
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