An Overview of How Smart Cities Use Emerging AI Technologies to Transform Urban Landscape

A few years ago, the concept of smart cities was considered a rare sci-fi utopia and got most urban dwellers excited about analog traffic signals and antiquated wastewater systems. In recent times, the idea of smart cities has come to a realization.

They have moved out of the research phase and entered the solutions stage. In many ways, the imaginary haven of technological marvels is becoming the perfect reality because of the generous support of artificial intelligence and machine learning. The main goal of smart cities is to ensure that technology evolves in ways that benefit urban residents.

According to a McKinsey Global Institute analysis, smart city technology can reduce the important quality of life by 10 to 30%, including the daily commute, health problems, and criminal incidents.

With the advent of artificial intelligence, a lot is happening in building smart cities. Infrastructure is streamlined better– vehicle connectivity, drone delivery, traffic management, sensors, parking, delivery/logistics, smart grid, etc.

By utilizing a variety of information and communications technology (ICT) applications, we can:

• Improve knowledge and innovation.
• Reduce expenses and resource consumption.
• Promote healthy living and working environments,
• Improve communication between the government and residents.

UN’s Predictions on the Development of Smart Cities

According to the UN, by 2050, metropolitan areas will house 68 percent of the world’s population.

• Projections also indicate that there could be an additional 2.5 billion people living in urban areas by 2050 due to urbanization, which is the gradual transition of people from rural to urban areas. Nearly 90% of this increase is expected to occur in Asia and Africa, according to a new United Nations data set.
• India, China, and Nigeria will together be responsible for 35% of the anticipated increase in the global urban population between 2018 and 2050. India is expected to have 416 million more urban residents by 2050, followed by China with 255 million and Nigeria with 189 million.
• Around 43 megacities with populations of more than 10 million are expected to exist worldwide by 2030, the majority of them located in developing regions. Yet, cities with fewer than 1 million residents—many of them in Asia and Africa—constitute some of the urban agglomerations that are rising the quickest.
• Sustainable development is becoming more and more dependent on the effective management of urban growth as the globe continues to become more urbanized, particularly in low-income and lower-middle-income countries where the rate of urbanization is anticipated to be the quickest.

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How AI and Its Emerging Technologies are Making Cities Smart

The simultaneous occurrence of urbanization and digitization has made it possible for urban AI to spread quickly into commonplace settings and people’s daily lives.

AI serves as a vital guide for modern travel habits. With the help of adaptive and autonomous algorithms, we can travel the routes we do now, whether by car, air, rail or even on foot and by bicycle. Also without digital recommendations, we might not even go to the locations we do go to.

Urban AI alters how we perceive the world in ways that have never been seen before. Through AI-led meta-analysis of previously separate datasets from urban mobility systems, geographic information systems (GIS), and urban closed circuit television (CCTV) systems equipped with face recognition.

In this article, we are focussing on the various emerging AI trends that are impacting the landscape of smart cities.

AI in Public Transit

Applications that harmonize the experience of its riders can be useful in cities with extensive transit infrastructure and systems. Via their smartphone apps, passengers of trains, buses, and autos may share real-time information about delays, breakdowns, and less congested routes. Encouraging other commuters to change their preferred travel routes, could relieve traffic in the future.

Cities can change public transportation routes and schedules and assign more precise infrastructure budgets by gathering and analyzing data on how people use the system.

One of the Smart City initiatives Dubai performed was tracking bus drivers’ health. As a result of this monitoring, the number of accidents caused by weariness and fatigue decreased by 65%.

AI in Citizen’s Safety

Data gathering from the environment is done via sensors, which are at the center of any IoT application. Although the primary function of all sensors is to collect data from the environment, each IoT application uses a different type of sensor depending on a variety of criteria. Some of the types frequently employed in IoT applications include pressure, bio, temperature, proximity, and imaging.

The majority of the currently accessible sensors, including ultrasonic, photoelectric, and optical sensors, are capable of carrying out simple tasks such as object detection.

The identified objects can be distinguished and counted with further integration with several additional technologies and algorithms. And that is a typical sight in automated smart manufacturing.

Did you know that the city of New Orleans is using AI to assist in the analysis of data from more than 325 cameras? By delivering pertinent footage and information and sparing the agency over 2,000 hours of manual effort, the system has assisted police in 70% of instances.

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IoT in Public Safety: High-performance computer and networking technologies that can withstand harsh environments are needed to make cameras, lamps, and crossroads intelligent. The most demanding edge applications can now be served by embedded and industrial-rated systems on chips (SoCs), computers on modules (COMs), and purpose-built devices, thanks to manufacturers.

Deep Learning and Predictive Analysis: Powerful computers learn by identifying patterns in data. Deep learning is being used by cities to analyze crime data and find significant patterns. A predictive police system was developed by the city of Manchester, New Hampshire, using data about crime, weather, and other factors that were superimposed on a map of the area. The technology makes predictions about where crimes will happen in a 500-foot area. It averages a 60% accuracy rate and has assisted law enforcement in a 28% decrease in overall crime.

5G Networks During Emergency: 5G networks will provide 10 times less latency, 50 times faster speed, and 1000 times more capacity than 4G networks. These extra seconds in an emergency circumstance can help save lives. The emergency services will be even more able to understand events as they happen in real time because of this huge increase in speed and bandwidth, which will enable them to coordinate and respond even more quickly.

AI in Power Grids

Power grid security and performance management could both be improved by AI and smart cities. Large amounts of data from smart meters may be read by smart grids which can then be used to assess and forecast load clustering and demand response. On these grids, prediction models can be built to predict the cost and demand for energy over a given period. Research suggests that these models are more accurate at forecasting price and load than the competition.

AI in Automation Systems

In key building places, sensors can be installed to track energy consumption and forecast consumer behavior. For instance, store owners and retailers can employ sensors to measure when people are most likely to come and use their establishments, as well as the locations where they tend to congregate. The data produced by the application of AI can assist in the production of reliable forecasts and the tracking of daily, weekly, and seasonal variations.

AI in Controlling Virus Spread

Some experts claim that the rapid spread of SARS-CoV-2 can be due to the movement of persons who have no or very minor symptoms, i.e., those who are ignorant of having the viral infection. They go on to say that this is why social distance is a crucial containment strategy.

Social isolation and periodic sanitization become the norm as a preventative strategy to safely carry out operations in places where it is essential for human interaction to occur, such as hospitals, shops, and some businesses.

AI in Emergency Management

Using a single framework and shared data pools, smart cities integrate these Internet of Things public safety equipment. Every agency now has a single pane of glass and a common perspective on events as they happen.

Additionally, it makes it possible for APIs to link public safety tech stacks to citizen data sources like social media, and smart building systems.

According to Juniper Research, smart cities might experience a 10% drop in violent crime and a 15% improvement in emergency response times.

AI in Controlling Natural Calamity

Natural calamities like wildfires, earthquakes, hurricanes, and terrorist strikes all take longer to develop and affect a much larger population than everyday incidents do. City managers can benefit from smart public safety technologies to help them plan for, respond to, and recover from significant, traumatic occurrences.

Daily information from public safety IoT and smart city technology provide decision-makers with the unbiased data required to pinpoint problems, enhance public safety, and create a stronger, healthier community.

Smart devices can aid emergency management in maintaining an accurate picture of the situation and making wise decisions during a severe catastrophe.

• Artificial intelligence can sift through the deluge of data and identify the most crucial signals from the background noise. AI can also assist with response triage by identifying regions that require the greatest resources and prioritizing crucial communications.
• Smart public safety technologies can be used to determine which areas require supplies and water after a disaster, which streets are dangerous to travel on, and which structures are sound.
• By giving the city the data it needs to prioritize reconstruction, streamline insurance claims, and make long-term strategic decisions, smart city technology can even hasten the recovery process.

It is not just a theory that smart public safety technology affects community resilience. With assistance from more than 30 local agencies, Rio de Janeiro established a central command center.

City authorities can map high-risk locations for floods and flood-related landslides and develop an early warning and evacuation system thanks to the shared information.

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Computer Vision and Sensory Applications

Deep learning also develops the models used by other devices to interpret data. Data scientists have, for instance, taught deep learning models to identify the sound of breaking glass. These types can be used by intelligent streetlights to listen for mishaps and break-ins.

A smart streetlight that hears glass breaking can automatically turn red, blink, and ask for help from the police.

In 2017, a criminal who fired numerous bullets at civilians in a Fresno, California, area was apprehended thanks to gunshot detection technology. When the police were almost immediately informed, they managed to apprehend the guy before he could escape.

It is pointless if first responders, commanders, and government officials cannot access public safety data, it is useless. It goes without saying that any smart public safety technology requires open hardware standards, open data frameworks, and shared data pools to succeed. Any kind of information comes with its perils. In particular, authorities are in charge of protecting sensitive criminal and medical data once they have collected it.

Governments must secure thousands of embedded devices and safeguard all personal and sensitive data as intelligent technologies proliferate.

Artificial intelligence is becoming more widespread due to the exponential increase in processing power at the edge. Self-perceiving, self-reacting, and self-intervening IoT devices for public safety are becoming commonplace.

When we talk about AI’s role in the context of smart cities, we indicate that AI may analyze personal data (for instance, to deliver and monitor power usage in a person’s home, or to track movements and deliver geo-targeted advertisements to potential customers passing through urban areas).

For safety and personalization purposes, it might also involve tracking and monitoring people as they move through public locations using facial recognition technology. There are several additional issues with privacy and data governance when AI is processing personal data. But, at the end of the day, smart Cities are a component of the solution to the escalating urbanization-related problems.

Final Thoughts

In a nutshell, technology for smart cities and public safety can sense, analyze, and take action thanks to artificial intelligence. From smart cameras detecting accidents and summoning EMS to microphones identifying gunshots, detecting the shooter’s location, and relaying it to first responders. Interviews can be written down and entered into evidence management systems using natural language processing.

[To share your insights with us, please write to sghosh@martechseries.com].