In a much-publicized judgement earlier this year, California regulators permitted the autonomous vehicle fleets of General Motor’s Cruise and Alphabet Inc’s Waymo to run paid taxi services at any time of day or night. But San Francisco is only one of three cities, the other two being Phoenix and Austin, that are allowed a driverless robotaxi service. That said, autonomous vehicles – which were slated to grow in value from $12.27 billion in 2018 to $31.17 billion in 2028 – have encountered many obstacles on the road to growth.

Need to dismantle barriers

A combination of technical, market and regulatory barriers stand in the way of vehicles transitioning from the current Level 1/2 autonomy – where “advanced driver assistance” helps people with steering and braking/ accelerating to different degrees – to full automation, where the vehicle drives itself.

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Doubts around technology:

Ironically, technology is one of the reasons why driverless vehicles haven’t progressed faster. The LiDAR sensors, radar systems, cameras and artificial intelligence software used in AVs must be capable of “sensing” the environment – traffic signals, road signs, other vehicles, and above all, pedestrians – and navigating it with high accuracy, even in difficult weather and traffic conditions. In areas with “mixed” or “unpredictable” traffic – where not just vehicles, but also pedestrians, kids at play, or even stray animals are on the streets – the performance of these technologies is yet to prove reliable.

Stalled legislation:

In the U.S., a six-year-old impasse that has blocked key AV legislation in Congress shows no signs of getting resolved. Questions around legal liability – who is to blame if an AV is involved in an accident – and insurance coverage continue to elude legislative consensus. Also, there is an urgent need to implement universally accepted standards for in-vehicle security and automated driving systems safety, such as UNECE WP.29 and ISO 22737.

Public hesitancy:

Resolving these two barriers will mitigate the third challenge, namely the lack of public acceptance, to a great extent. It is worth noting that consumers’ enthusiasm for all things digital doesn’t quite extend to driverless vehicles. In a 2020 survey of nearly 1,200 Americans, 48 percent said they would never travel in an AV, and 20 percent said these vehicles would never be safe. But 58 percent thought AV safety was about ten years away; hence improving the technical, safety, and security aspects of the vehicles would go a long way in building trust and adoption.

The U.S. needs to speedily address these issues because growth in AVs is beneficial for individuals, the automobile industry, and society in general. For drivers and passengers, these vehicles make the on-road experience safer, easier, and more enjoyable. More than 40,000 people die in road accidents every year in the U.S., of which more than 90 percent are caused by human error. AVs can make a huge difference to safety since they are 50 percent less likely than humans to be involved in a collision. People can use the time inside the car to work, connect with others, or entertain themselves. Since they can be equipped with sophisticated features, self-driving vehicles could be a very good option for the elderly or those with mobility challenges. Indeed, with autonomous driving systems evolving rapidly towards Level 3 and Level 4 autonomy, the extra features, components, and related commercial solutions could add $300 billion – $400 billion to the passenger car market by 2035.

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Also, because self-driving vehicles are powered by electric or hybrid engines, they lower greenhouse emissions and further help the environment through their optimized driving behavior, energy-friendly acceleration, and lower fuel consumption.

AI can help in Fixing the Safety in Autonomous Vehicles

Artificial Intelligence is emerging as an important tool for improving the safety of autonomous vehicles, thereby paving the road to adoption.

Safety testing is a highly complex activity, requiring massive financial resources and time; testing a vehicle’s performance in a natural driving environment, reflecting “real” conditions, may require hundreds of billions of testing miles. But now, breakthrough research suggests that using AI to train vehicles could potentially slash the testing miles requirement by 99.99 percent. The problem in testing AVs is that safety-critical data is a minuscule proportion of the overall massive data, which makes safety-critical events very rare during testing; to address this, researchers isolated the small-sized safety-critical data and used only that to train neural networks, dramatically accelerating the learning (and therefore, testing) process. Although the study focused on road geometry and moving objects, the model can be extended to test for extreme weather events and even for complex road environments (many highways, intersections, etc.) which are beyond the scope of existing testing methods.

Autonomous vehicles are the future of mobility; to seize it, the U.S. must address the technical, regulatory, and consumer barriers to adoption. AI can be a key enabler of this agenda.

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

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Japan’s Nidec Corporation and Brazil’s Embraer announced an agreement to establish a joint venture company, called Nidec Aerospace LLC (hereunder “JV”), to develop Electric Propulsion Systems for the aerospace sector. The transaction combines the complementary synergies and distinct areas of expertise of two world-class engineering companies to spearhead a new era of air mobility.

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To be unveiled at the 54^th biennial Paris Air Show, the JV aims to unlock new opportunities by providing an agnostic portfolio of products and services worldwide, driven initially by the growth of the Urban Air Mobility (UAM) industry.

Nidec Corporation, the world’s leading comprehensive motor manufacturer, will be backed by Embraer’s more than 50-year history of complementary aerospace experience to design, certify, produce, and commercialize next-generation electric propulsion systems based on well-proven technologies suitable for powering more efficient and greener aircraft models. The JV will develop and manufacture the Electric Propulsion System for electric Vertical Take-Off Landing (eVTOL) vehicles, with the aim of providing the system to non-eVTOL vehicles in the future.

Nidec Aerospace will be jointly owned, with Nidec owning a 51% share and Embraer the remaining 49%. The headquarters, located at Nidec Motor Corporation (NMC) in St. Louis, Missouri, USA, will be supported by both companies’ existing industrial footprint in Brazil and Mexico.

“Technological innovation will be a key contributor to the International Civil Aviation Organization (ICAO)’s commitment to carbon neutrality by 2050. The JV is a natural extension of both companies’ respective and continual investments in green technologies across multiple industries to accelerate global carbon neutrality,” said Michael Briggs, Senior Vice President and President of the Motion & Energy Business Unit, at Nidec. “We are proud to be partnered with Embraer, and are confident that Nidec Aerospace will spearhead the electrification of aircraft with our shared drive, complementary expertise, and wide breadth of technical and manufacturing capabilities.”

Before starting our collaboration, both Nidec and Embraer have a long history of technological and business collaboration with other global partners and have been continually exploring a range of sustainable concepts, prospecting technologies, and high-growth-potential markets to deliver stronger value to their respective stakeholders.

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“Innovation is our future growth driver and a key pillar of our strategic plan. That’s why I’m extremely excited about this strategic partnership with Nidec to develop agnostics solutions for aerospace sector,” said Francisco Gomes Neto, President and CEO at Embraer. “Demand for electric propulsion systems is growing exponentially in the aerospace sector, and we are very confident that Nidec and Embraer together can accelerate the development of advanced products to enable the future of sustainable aviation.”

The JV’s Electric Propulsion System launch-customer will be the eVTOL manufacturer of Eve Air Mobility (NYSE: EVEX, EVEXW), an independent company well-positioned to be a global leader in the Urban Air Mobility (UAM) segment by delivering an effective and sustainable new mode of urban transportation.

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

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Quantum-Systems Inc., a leader in electric vertical take-off and landing (eVTOL) aerial intelligence solutions, announced its inclusion in the second set of projects to receive funding for the United States Department of Defense (DOD) pilot program to Accelerate the Procurement and Fielding of Innovative Technologies (APFIT).

The announcement comes after the Office of the Under Secretary of Defense for Research and Engineering (USD(R&E)) published an official release outlining the 11 DoD program offices that will receive FY23 APFIT funding, with U.S. Special Operations Command (USSOCOM) awarding Quantum-Systems Inc. $20 million.

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“We are honored by DoD’s decision to allocate FY23 APFIT funds to accelerate procurement of our Vector fixed-wing, eVTOL unmanned aircraft system. This will allow us to increase manufacturing capabilities and get our mission-critical technology into the hands of more warfighters sooner, ” said David Sharpin, CEO of Quantum-Systems Inc.

Established by Congress in the Fiscal Year 2022 National Defense Authorization Act, APFIT is a competitive, merit-based program with the goal of helping companies to expeditiously transition and field technologies.

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

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“EP Systems is thrilled to partner with REGENT to provide the Energy Storage System and the power management system for this innovative Wing in Ground effect vehicle”

REGENT has already obtained $8 billion in orders from major airlines and leading ferry operators in five different continents. REGENT’s 12-passenger seaglider, Viceroy, is expected to enter service by mid-decade.

The partnership between REGENT and EP Systems is a significant development, as it combines the expertise and resources of two industry leaders to create an innovative and environmentally friendly solution bringing a new level of efficiency and sustainability to the transportation industry.

The seaglider is poised to revolutionize transportation by offering high-speed, zero-emission transportation along coastal water ways. The use of state-of-the-art power integration technology will not only make the seaglider able to service routes up to 180 miles on a single charge, but also enable it to operate quietly, reducing noise pollution. This will have significant benefits, particularly in areas where noise abatement is a concern, such as near residential areas and wildlife habitats.

“We are pleased to have EP Systems on board as our battery provider for our full-scale prototype seaglider,” said REGENT’s CEO Billy Thalheimer. “Their expertise in producing high-quality, efficient battery technology is second to none, and they share our deep commitment to safety. We are confident that their battery solution will help make the seaglider a true game-changer in the transportation industry.”

EP Systems is known for its expertise in developing and implementing power integration solutions that meet the needs of today’s transportation industry. Its technology will enable the seaglider to fly with a longer range and higher speed while reducing operating costs and maintenance requirements.

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“EP Systems is thrilled to partner with REGENT to provide the Energy Storage System and the power management system for this innovative Wing in Ground effect vehicle,” said Nathan Millecam, CEO of EP Systems. “Our technology will enable the vehicle to achieve greater efficiency, reliable paced operations, and a sustainable solution for this groundbreaking form of transportation.”

EPS adds REGENT to a growing list of customers, which includes NASA, the FAA, Boeing, Diamond Aircraft, Plana, Safran, Supernal and VoltAero. The company also provides its electric powertrain system to X-57, Boeing’s CAV (Cargo Air Vehicle) and Bell Helicopter’s hybrid Advanced Air Mobility aircraft (Bell Nexus).

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

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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].

The post An Overview of How Smart Cities Use Emerging AI Technologies to Transform Urban Landscape appeared first on AiThority.

It’s been a while since the concept of smart cities gained popularity and is on the brink of becoming a sophisticated reality. A growing number of studies highlight the characteristics of smart cities since the idea was first proposed. The key elements that ought to be taken into account to make a city more intelligent and sustainable, however, are still up for debate.

Over the years, sustainable development has gained popularity and has an impact on urban development and planning. The continued concentration of the world’s population in cities suggests that challenges of sustainable development are becoming more and more important to address.

Rapid urbanization, as well as social, environmental, and economic issues in cities, have made sustainable urban development a top priority. An increasingly common strategy for ensuring sustainable urban development is the smart city.

The improvement of human life, transportation, health, energy, and education is significantly aided by smart cities.

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Many leaders have pointed out the significance of smart cities in achieving urban sustainability to have a healthy and sustainable future. The aforementioned elements demonstrate how important smart cities are for sustainable urban growth.

The process of enabling smart cities is difficult and countries are finding strategic ways to grapple with it. In this article, we are looking at the biggest drivers and the barriers for smart cities that are adopting AI for suitability.

AI for Sustainability – Drivers and Barriers

Cities are home to 56.2% of the world’s population and the challenges are felt everywhere. Increased urban efficiency benefits communities all around the nation and the world, from commuting and congestion to economy and supply networks.

The most basic requirement for smart cities to function smoothly and survive is a combination of funding, state-of-the-art technology, and partnerships. The necessity for legislative adjustments, the scarcity of resources, political unpredictability, and fragmented funding arrangements may deter investment in smart cities.

• According to the most recent research, the market for smart cities worldwide is anticipated to develop at a CAGR of 20.5% and reach $2.5 trillion by 2025.
• Statista predicts that global investment in direct digital transformation to reach $6.8 trillion by 2025, and by 2022, 40% of cities will be using digital space-planning tools. Investments in digital transformation and smart city deployments are also continuously rising upward.

The effective implementation of smart cities is influenced by a variety of factors including cutting-edge technologies like edge computing, blockchain, artificial intelligence, and 5G and 6G networks that are seen to be crucial.

Certainly, implementing new technology and readjusting the global business ecosystem present us as a society with many obstacles.

In addition to overcoming severe legal, regulatory, and compliance obstacles, we also need to include important players in cost management and closing the digital and financial divides across several global marketplaces.

In addition, cyber threats have resulted in significant security lapses in recent years and are predicted to rise along with the widespread adoption of developing technology.

The search for a long-lasting solution will necessitate a worldwide strategy as trust and privacy continue to be major challenges.

Experts have also noted considerable gaps in interoperability and portability, which would necessitate the creation of global standards, certifications, metrics, and quality assurance initiatives.

Top Key Technological Drivers

Traffic management

Maneuvering through traffic is an art but just imagine if this becomes a new normal for the rest of our lives. In the current times, most city dwellers detest traffic and the very idea of being stuck on the road haunts them, but thanks to technology, there are some potential alternatives.

For instance, intelligent traffic light systems can be utilized to reduce congestion and public transportation routes can be changed in real-time in accordance with demand.

AI has been the savior for the residents of the Chinese city of Hangzhou where the AI-based smart “City Brain” monitors every single vehicle and has helped to bring down traffic jams by 15%.

By giving artificial intelligence (AI) authority over a metropolis, City Brain operates. Massive volumes of data are collected, processed by supercomputer algorithms, and then fed back into devices all around the city.

According to TomTom, a navigation company, Hangzhou is ranked 30th internationally and fifth in China for congestion. The government worked with Alibaba and its cloud computing platform to eliminate this.

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Digitized Waste Collection

In its native Spain, the mobile and broadband provider Telefonica has made significant investments in smart city technology. With over 23,000 tonnes of waste generated in 2016 and 12% less in 2017, Telefónica is digitizing waste management in Europe and Latin America. Refuse collectors don’t have to waste time going to bins that are only partially full since sensors linked to the rubbish bins report how full they are, in real time.

This means that KPIs can be tied for closer impact by keeping a tab on the number of dumpsters that are nearly full and won’t be emptied within the next few hours.

Comparing this to more impersonal metrics like the number of garbage collection trucks on the road is much more insightful.

The new platform is now operational in Colombia and Spain, and it will soon be available in the other 15 countries where Telefónica has operations.

In 2016, Telefónica produced more than 23,500 tonnes of garbage, of which 66%, or more than 15,500 tonnes, were cables made during the transformation of the internet, of which 98% were recycled.

A Safer Environment

Cities may use technology to increase resident safety and speed up incident response times because Wi-Fi access is so widely available, IoT technologies are advancing, and CCTV cameras are becoming more commonplace.

For instance, in New Orleans, real-time video data from Bourbon Street is examined to enhance public safety by better tracking and allocating personnel on the ground.

Energy efficiency

Smart cities use technology to closely monitor real-time energy use, cut energy use, and invest in sustainable energy sources.

The city of Schenectady, New York is enhancing its street lighting by upgrading to LED technology, which enables the lights to be changed or muted depending on real-time data, and residences in Amsterdam are receiving smart energy meters that are intended to encourage decreased energy consumption.

Better Citizen Connectivity

Smart city technology encourages inhabitants to participate more, which is one of its most fascinating features. Examples include apps that make it easier for residents to report neighborhood problems or social networking sites that let neighbors interact and exchange resources.

Another illustration is a low-cost environmental testing kit that encourages locals to gather environmental data. The Smart Citizen Kit can be positioned on windowsills and balconies to collect information about the neighborhood’s environmental conditions, such as noise and air pollution.

The Smart Citizen Kit can enable communities to create regional noise and air quality maps or use it to bring attention to and discover solutions for problems that are important to your neighborhood.

It can also be employed as a tool for gathering and analyzing data. Real-world deployments will help you better understand how people, the environment, and technology interact.

Real-time data generation and awareness raising about urgent environmental challenges will enable communities to look for solutions.

A crowd-sourced map of data from all across the world is effectively created by the data being transmitted to an online platform.

Top Barriers to Smart Cities

Cities cause new types of physical issues such as resource depletion, air pollution, trouble managing waste, traffic jams, and inadequate, failing, and aging infrastructures, among others. Let’s take look at the most common barriers to smart cities adopting AI sustainability.

Lack of collaboration

Create a procedure that brings together individuals with various experiences, skill sets, and viewpoints once your plan is in place. Create a sensible project performance baseline first. This will make it easier to decide who and what is required to deliver the needed functionality.

For example, careful alignment of all stakeholders, regardless of duty or area of expertise, should support airport sustainability efforts. To create more effective transportation hubs or smart infrastructure for large regions, it is necessary to combine supercomputing with transportation resources.

Whether you’re discussing wearable medical devices or smart airports, crucial technology-building components like antennas, hybrid flexible electronics, wireless power, human-machine interfaces, and more must be considered.

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Find ways to involve everyone from the design of the system’s architecture through development, deployment, user experience, end-user adoption, and continuing maintenance.

Transparency in Deployment plans

By using smart technology, there is no such thing as too much communication. To ensure that everyone understands the objective and purpose of the plan, be clear about what you’re going to do upfront and provide frequent updates.

The general population must be able to properly understand how the implementation of smart technology would enhance their lives without posing unnecessary dangers to their security or privacy.

Interoperability, above all

The need to connect one smart device to another will force an enormous web of interconnectivity across a smart IoT ecosystem as smart objects multiply.

Interoperability standards, which do not yet exist, are needed to ensure that objects can communicate and share relevant information. Although plug-and-play functionality is the ultimate objective, the majority of smart devices in our homes still don’t function in that way.

It’s important to not just acknowledge high-level data security, interoperability, and redundancy but also addresses them. The challenges to establishing universal connectivity must be addressed in every conversation about smart everything.

It’s a fact that standards for communications, security, and interoperability will continue to be the prime focus, it’s important not to lose sight of the broader vision of sharing communities, where the collective access to resources is simple.

Mass Acceptance

Till the time there is a lack of mass acceptance, cities, and project teams must gather proof of results and success stories that support initial hypotheses while establishing a schedule for ongoing funding and innovation. Although technology will continue to advance, for it to be widely accepted, people must accompany it.

Examples from the real world highlight the advantages and provide helpful understandings of some commonalities and fundamental components that apply regardless of industry or application.

With the assistance of groups that promote collaboration, components including sensors, actuators, data analytics, and communication linkages are being used in Singapore and throughout the United States.

Let’s take a look at a few more examples of barriers.

• Open data and its availability are a problem in smart cities, which may delay the delivery of smart city services to citizens and businesses.
• The growth of smart cities may be hampered by limited openness and ambiguous lines of political accountability in the provision of the majority of services. The absence of openness runs the risk of alienating the very people that smart city technology is meant to help.
• One of the major obstacles to the development of smart cities is the lack of uniformity across metrics (such as smart technologies, security, privacy, quality of life, environmental sustainability, physical infrastructure, mobile networks, etc.).
• Absence of a more sustainable and conscious city (e.g., traffic control, parking availability notifications, petrol emission reductions, etc.) results in a lack of better living conditions and experiences for all.
• Lack of technology integration and the convergence of heterogeneous networks (such as Bluetooth, WLAN, and heterogeneous cellular networks like 3G, 4G, and 5G, etc.) could be obstacles to the development of smart cities.
• The creation of smart cities is sometimes plagued by privacy and security concerns (e.g., dangers from hackers and viruses, poor privacy, exorbitant expenses, etc.
• Population growth could be a problem for the development of smart cities.
• The enabling or revolutionary technological knowledge that would be required for the establishment of smart cities is not available to planners and policymakers.
• Geographical imbalance can impede the growth of smart cities.
• Citizens’ lack of involvement in understanding how precisely smart cities might look in their experiences is evident. The submission and evaluation of innovative ideas for smart city design from the public should be encouraged.
• The growth of smart cities is hampered by the high cost of IT, professionals, consulting, installation, operation, and maintenance, as well as training.
• Absence of IT infrastructure (such as solar-powered electricity systems and cloud computing) and artificial intelligence capabilities (e.g. smart communities, smart energy solutions, e-health, intelligent transport system, smart grids, etc.)

Final Thoughts

In the end, there will be a flood of new business models, technological advancements, and industry changes when a single smart city can communicate with a neighboring city as readily as with one that is across the country.

The post AI for Sustainability: An Overview on the Biggest Drivers and Barriers to Smart Cities appeared first on AiThority.

The evolution of Smart Cities has been inspiring and remarkable to watch. In the recent past, a typical resident might not have found the technical description of a smart city all that enticing, but today, citizens are more aware and more conscious. They are far more concerned about the environment and climatic changes.

Government and civic agencies across various countries, with the help of state-of-the-art artificial intelligence technology, are focusing on reducing carbon footprints, improving infrastructure, and meeting the sustainability goals of smart cities.

Did you know that according to a report by McKinsey Global Institute, ‘Smart Cities’ have the potential to refine the basic quality of life by 10-30%? It can reduce crimes, lower carbon emissions, better health management and improve traffic management and deliver an enhanced quality of life.

McKinsey Global Institute’s report stated that cities house more than half of the world’s population, and another 2.5 billion people are predicted to move there by 2050.

Today, artificial intelligence and the Internet of Things (IoT), the two concepts that have a major role to play in the development of Smart Cities, are better understood.

What are Smart Cities and Where Does Technology Come into Play?

Let’s begin by understanding the definition of a Smart City. Smart Cities are an intelligent culmination of data and digital technology. They are synonymous with intelligent economic and civic infrastructure with minimal carbon footprints.

It ensures that its citizens enjoy cutting-edge technology, utility, and mobility while eliminating bureaucratic red tape. At the end of the day, a Smart City’s ultimate goal is to improve people’s quality of life, simplify living, boost economic growth, and contribute to its long-term development.

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But, is it enough for cities to just fall under the Smart Cities bracket and do little to meet their sustainability goals? That’d be a very unlikely situation. Smart Cities can only be successful if they are built keeping the people as well as the environment in mind.

According to Unesco,

“A smart sustainable city is an innovative city that uses ICTs (information and communication technologies) and other means to improve quality of life, the efficiency of urban operation and services, and competitiveness while ensuring that it meets the needs of present and future generations with respect to economic, social, environmental as well as cultural aspects.”

AI in Smart Cities

From more accessible, efficient services to lowering people’s overall carbon footprint, the many smart city technologies now available and on the horizon might cut expenses, increase safety, better protect the environment, and improve our quality of life.

Traffic Flow Management

Intelligent Traffic Management systems can help to alleviate traffic congestion by warning vehicles of bottlenecks and delays. Using Deep Learning algorithms, it can predict and reduce traffic, hence lowering carbon emissions. Traffic infraction detection systems and AI-enabled cameras can drastically minimize road accidents.

AI is used to evaluate real-time traffic data from cameras and IoT devices, such as vehicles like cars, buses, and trains. It recognizes patterns in data and decreases safety hazards and reoccurring accidents, as well as controlling traffic light systems.

Artificial intelligence is rapidly transforming the world around us, and smart city technology, such as parking management and traffic control systems, is one of the most effective answers it offers. With the use of artificial intelligence, one may properly forecast the flow of people, cars, and objects at various locations of interconnected transportation networks.

Smart Parking Spots

Parking has always been one of the major concerns for urban residents, and spending even five minutes looking for a parking spot can be overwhelming. Smart parking spots will allow commuters to reserve parking reservations through a mobile app, reducing the amount of time spent looking for parking spots, cutting urban traffic, lessening our carbon footprint, and conserving gasoline.

AI video analytics can detect the number of vehicles and identify parking lines, thus helping in predicting vacant parking spots. This system comes especially handy when a big public event, concert, or game is about to take place and there are high chances of congestion and struggle to park. AI can assist in identifying likely busy regions and recommending the best parking spots. It can assist drivers in avoiding traffic and saving time.

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By now, several countries are already leveraging intelligent parking systems to help their citizen save time as well as money. The parking system first spots vacant parking and notify through an app or an indicator.  It can also assist in locating available parking spaces in congested places where traffic flow is frequently excessive.

This innovative parking solution collates data from different devices including sensors and cameras. Most of the time, these devices are embedded into the parking lots or are somewhere in proximity to instantly locate vacant spots.

Alternative Transportation

Infrastructure data is truly a blessing. It empowers smart cities as well as different modes of transportation. Today, people have the luxury to opt for alternative transportation like e-bikes, and electric vehicles. Benefit from the usage of 4G, 5G, and IoT sensors to better analyze traffic patterns, trends, and effects through AI, cutting travel time, reducing unproductive idling, and lowering total climate impact.

In electric cars, AI assists in the control of energy consumption, safety, security, and the construction of a pollution-free eco-friendly environment, which is a wish of today’s and tomorrow’s civilizations.

Recently, computer giant Acer launched e-bikes powered by advanced artificial intelligence. The bike, aimed at urban commuters, weighs only 16kg and has been calibrated for “stable and nimble riding,” according to Acer. The intelligent ebiiAssist learns from the rider’s pedaling force, riding circumstances, and chosen level of help to provide a more personalized experience.

Energy Management

Is it even possible to fathom a smart city without thinking of a smart Energy Management System (EMS)? Now the next question is, what is energy management based on? Mostly, it is based on cutting-edge climate and geospatial technology powered by AI and data analytics. They have the ability to improve our reaction to climate change as well as the overall environmental quality of smart cities.

Energy Management System is a software-based solution that assists companies and businesses in monitoring, controlling, and optimizing their energy usage. Some of the top players in the global energy management systems market are IBM Corporation, General Electric Co., Cisco Systems Inc., and Siemens AG.

Consumers and businesses are becoming more conscious of the environmental impact of their actions and are seeking for solutions to lower their carbon footprint. This is driving the use of EMS solutions as a means of reducing energy consumption and meeting sustainability goals. The growing popularity of smart homes and buildings is driving the use of EMS solutions in the building automation market.

According to Vantage Market Research, the global energy management systems market was valued at $36.4 billion in 2021 and is predicted to rise at a compound annual growth rate (CAGR) of 15.8% from 2022 to 2028.

• Because of their flexibility, scalability, and cost-effectiveness, cloud-based EMS solutions are gaining popularity. These solutions allow for remote monitoring and control of energy consumption, making it easier for businesses to optimize their energy consumption.
• MS solutions include the Internet of Things (IoT) and artificial intelligence (AI) technology to enhance energy efficiency and save expenses. IoT sensors can give real-time data on energy consumption, which AI algorithms can analyze and discover areas for improvement.
• With the growing use of renewable energy sources such as solar and wind power, EMS solutions are being developed to control and optimize their utilization. Integration with smart grids and battery storage systems is required to ensure an efficient and dependable energy supply.
• EaaS models are gaining popularity, especially in the commercial and industrial sectors. These models enable enterprises to outsource their energy management to third-party providers, who deploy EMS technologies to optimize energy consumption and save expenses.

Water Pressure and Leak Detection 

According to the American Water Works Association, the 237,600 water line breaks that occur in the United States each year cost public water utilities around $2.8 billion.

According to the American Society of Civil Engineers, aging, leaking pipes drain 7 billion gallons every day from our water systems. The World Bank estimated that non-revenue water (NRW) – the cost of water lost due to leaks, as well as standard theft and billing problems – is approaching $14 billion globally.

The World Bank estimated that non-revenue water (NRW) – the cost of water lost due to leaks, as well as standard theft and billing problems – is approaching $14 billion globally.

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These numbers are worrisome. But, we have smart technologies to fix it. In the past decade, smart water meters have been the highlight of this evolution. Water losses in municipal water systems could be drastically reduced with the help of sensors and modern artificial intelligence (AI) technology.

• The technique, developed by researchers at the University of Waterloo in partnership with industrial partners, can detect even minor leaks in pipelines. It uses sophisticated signal processing techniques and artificial intelligence software to detect leaks in water pipelines via sound.
• The audio signals are captured by hydrophone sensors, which may be readily and inexpensively put in existing fire hydrants without the need for excavation or shutting them down.
• Aside from the economic implications of losing treated water, chronic leaks can pose health risks, cause structural damage, and degrade with time.

Air Quality Prediction and Automated Actions

Air pollution has a negative impact on millions of individuals around the world and global solutions are the only way to address these global issues. Artificial intelligence is a practical technique to dealing with and reducing air pollution. AI can collect sensor and satellite data and assist academics in the blending of climate models.

Let’s take a look at how artificial intelligence-based solutions for cleaner air.

• Artificial intelligence has the potential to improve data collecting and qualitative measurement. AI can detect patterns in data sets to aid with analysis.
• AI can forecast future air quality and direct relevant agencies to take the necessary actions ahead of time.
• Artificial intelligence can provide maintenance insights for sensors and other equipment.
• AI and IoT provide recommended tools for real-time monitoring of air pollution. AI technology can swiftly and correctly identify sources of air pollution. Smart sensors, for example, can identify the source of a gas leak in a company and effectively apply corrective measures.
• AI can aid in the reduction of air pollution in the automotive zone. AI technology allows autonomous vehicles to be fuel-efficient. AI-powered traffic signals can potentially help to reduce air pollution. We can utilize machine vision to adjust to traffic flow, reducing driving time.

AI technologies can greatly help government organizations and commercial firms by monitoring air purity levels and alerting personnel if air quality falls below a specific threshold.

• IBM researchers are collaborating with the Beijing government to use artificial intelligence to combat air pollution and monitor environmental health. Machine learning and cognitive abilities are being used by researchers to increase forecast accuracy. AI can help predict air pollution levels 10 days in advance. Scientists are combining artificial intelligence (AI) technologies to do scenario analysis and take necessary measures such as traffic control, plant shutdowns, and more.
• Scientists at Loughborough University in the United Kingdom have created an AI-based algorithm that predicts air quality in advance. The model examines sensor data and assists policymakers in understanding the reasons and methods for reducing air pollution.
• CleanAir. AI is a Canadian IoT firm that provides air filtration for homes and buildings using AI-based technology. The startup employs AI and IoT to provide actionable information on indoor and outdoor air quality, deliver cleaner air, and save energy.

Final Thoughts

A smart city has a wide range of components, and each one has its effects on the quality of urban dwellers. How we live, work, and play will change as smart cities grow and become more connected. From weather monitoring and pollution management to saving energy and water and waste management, Smart Cities may be a work in progress but they are gradually becoming the epitome of urban living.

The post AI in Smart Cities: How Innovative Technology is Enabling Smart Cities to Meet Their Sustainability Goals appeared first on AiThority.

Artificial intelligence (AI) is ubiquitous and is becoming more accessible to businesses as they digitally modernize their operations, thanks to new IT breakthroughs. AI is currently serving as a critical enabler in the energy, and transportation sector as it moves to a reliance on renewable energy.

In recent times, almost all industries are leveraging artificial intelligence, machine learning, deep learning, or IoT to realize the dream of a smart and sustainable city. In this post, we are deciphering how AI has played a vital role in the development of transportation, energy, and environmental systems.

AI in the Development of Transportation – 5 Use Cases

Over the past few hundred years, the transportation sector has witnessed numerous transformations and revolutions, and we are now at a point where significant advancements in the form of artificial intelligence are being made.

AI is capturing the attention of transportation executives all around the world, whether it is through self-driving cars for greater dependability, road condition monitoring for increased safety, or traffic flow analysis for greater efficiency.

With the worldwide market expected to reach $3.87 billion by 2026, many in the transportation industry have already recognized AI’s incredible potential.

With the use of cutting-edge technologies like computer vision and machine learning, businesses can influence the future of transportation by enhancing passenger safety, lowering the number of accidents, and easing traffic congestion.

For instance, in Glasgow, technology including deep learning and machine learning track parking violations, traffic density patterns, and vehicle dwell times. These have a major role to play in the creation of smart cities.

The following use cases of AI in transportation explain the market’s growth and the need for businesses to utilize the technology more than ever.

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Pedestrian detection

Walkers can be incredibly unexpected, particularly in the case of road traffic; pedestrian detection is a major issue in computer vision and pattern recognition. They are one of the biggest threats to the success of self-driving automobiles because they are so unpredictable.

A computer automatically identifying pedestrians in images and videos without human assistance sounds amazing but the more important thing is that a system can correctly distinguish a human from another object. And also comprehend what a pedestrian is going to do next, like ‘Will they cross the road?’ rather than only being able to recognize individual human traits like beards and noses.

Different features like texture-based features, gradient-based features, motion-based features,  and shape-based features.

Self-driving cars

Self-driving cars have been around for quite some time. General Motors were the pioneer in self-driving vehicles when it launched it in 1939. Today, with the help of artificial intelligence, technology has advanced and is smarter than ever.

Neural networks and specialized algorithms are used in autonomous cars. These object detection techniques are based on machine language (ML) and artificial intelligence (AI). They assist with data gathering, object analysis, and making informed judgments while driving.

Using computer vision techniques like object detection, intelligent systems can be built to decode and comprehend visual data, effectively enabling a vehicle to drive it.

Massive volumes of data are fed into the algorithm, which is then trained to recognize certain objects and given instructions to perform the appropriate maneuvers like braking, turning, accelerating, decelerating, and so on.

Tesla recently produced self-driving electric vehicles with autopilots, which allow for automatic steering, braking, lane change, and parking activities.

These automobiles also have the potential to lower pollution globally, which would be a significant improvement over fuel-powered cars.

Many of the world’s largest cities now have autonomous vehicles on the road. Even heavy-duty vehicles without drivers that are capable of transporting cargo over great distances have been created.

This has considerably decreased transportation costs while also lowering the number of fatal accidents, many of which are caused by human mistakes.

Traffic Management

Traffic management may not seem like a humongous process considering vehicles follow a set pattern of stopping at red lights and moving at green lights, but the number of people who are killed at traffic signals will shock you. Every year, at least 1,000 people are killed by cars that run red lights.

A smooth traffic flow impacts a nation’s economic development while the safety of its road users is greatly increased.

Better traffic flow analysis is now possible because of artificial intelligence techniques like machine learning and computer vision. AI can assist in eliminating traffic congestion and removing bottlenecks that are otherwise congesting the economy and highways. CCTV cameras may identify risky situations and other irregularities, as well as reveal information about rush hours, choke areas, and bottlenecks.

The algorithm can help in:

• Accurately track and count freeway traffic.
• Analyze traffic density in urban settings.
• Spot other anomalies.

The details from these algorithms help towns and cities to understand the status and at the same time create more effective traffic management systems and enhance road safety. And so, town planners can therefore significantly cut urban traffic and pollutants.

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AI in Energy Efficiency – Top 3 Use Cases

There are many ways AI can be used in the current energy system. Power providers, for instance, can find flaws before they cause failures and dangers. To combat the dangers and failures, updated solutions are required. The necessity for supply management, coordination, and forecasting increases as more electricity is required. And that is what artificial intelligence can provide.

The desire to adopt low-carbon energy transition has increased on a worldwide scale. More renewable energy capacity is now installed by service providers than nuclear power and fossil fuels put together.

Distributing storage is necessary for this process, as it offers a complex networked infrastructure for renewable energy. AI-powered “smart consumption” solutions alter how people use and conserve energy. Using the previous data, decentralized power grids can be created to balance energy inputs and outputs.

Smart Grid

A smart grid is a novel approach to energy-efficiency networks that benefits from the two-way exchange of data and electricity. The major difference here is the adoption of AI, Cloud, and digital technologies that assist control and self-regulation marks the fundamental departure from customary networks.

For example, AI’s self-learning, flexibility, and computation capabilities have tremendous promise to handle renewable energy’s intermittent nature.

One of the most notable examples of the smart grid is the collaboration between London’s National Grid and IBM’s cloud-based analytics. Preventive and predictive maintenance are offered by the smart grid, which are essential components of the grid’s operation. To sum it all up, an AI-powered smart grid shows increased resilience and improved security for the grid, assisting in more accurate forecasts.

Subsurface geophysical data is being analyzed using cloud-based AI tools. The use of a cloud-based source to keep track of data gives faster and more precise solutions. Drilling processes in the oil and gas business could be improved with the assistance of AI in tracking and locating subterranean oil deposits.

With smart grids, you can:

• Organize, store, and distribute energy from these sources into a consistent stream.
• High-demand strains may be anticipated and spread across numerous plants and substations thanks to predictive analytics in smart systems.

Machine Malfunction Prediction

The Internet of Things (IoT) was created to link and make work easier by allowing access from anywhere. When implemented in the oil and gas business, IoT plays a critical role in cost optimization. It increases safety by allowing for predictive maintenance, performance forecasting, and real-time risk management.

IoT collects data by connecting to all types of machinery. Sensors will be able to identify machine malfunction before it is noticed by humans. With this, accidents can be prevented and a ton of money can be saved by looking at the damage predominantly before the machine collapses.

To avoid unplanned downtime and excessive effort, IoT-based predictive maintenance allows you to systematically organize the appropriate maintenance and inspection routine. With predictive maintenance, avoidable expenditures can be significantly decreased, as can the amount of time the machinery or equipment is down for repair.

IoT-based predictive maintenance can:

• Increase asset utilization.
• Enhance the efficiency of field crew assets.
• Improve safety.
• Reduce maintenance costs.

Digital Twins

One of the most significant areas where digital twin (DT) technology may play a critical role is asset management, which includes monitoring and maintenance, project planning, and lifecycle management. In such a case, Digital Twins allow Energy & utility firms to address issues. A procedure, building, or physical object’s multidimensional visual depiction is referred to as a “digital twin.”

As real-time virtual models, these digital twins offer more research opportunities than simulations. Digital twins aid in studying wind turbines and power generation in the energy and AI sectors.

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A digital twin created using AI could be a step towards improving the servicing, testing, upkeep, and optimization of the energy network, whether it be conventional or renewable.

Digital twins’ effectiveness can be increased by artificial intelligence by offering insights that go beyond what can be obtained from physical sensors.

To improve the city’s air quality and resilience to climate change, Mendoza, Argentina, plans to map its green infrastructure and trees using a city-scale digital twin in 2022.

GenMap immediately identified one million trees, resulting in the creation of a digital twin of Mendoza’s green infrastructure. They digitalized, geo-referenced, and obtained the proportions of each tree as well as the neighboring roads and walkways using Bentley Systems’ mobile mapping technology.

Digital twin solutions have a positive or, at the very least, a lessened influence on the environment and ecosystems, including wildlife.

To study the influence of certain projects and structures, such as wind farms, on local ecosystems, digital twins can be utilized to generate a virtual clone of a natural environment.

AI in Environment Quality System

The efficient use of natural resources including water, minerals, fossil fuels, and others will be essential to the sustainability of the planet and civilization.

With a rapid increase in population and urbanization, a significant amount of natural resources must be made available for various uses, leaving behind a sizable amount of resource-rich trash. Despite theories in the literature based on the recovery of these resources, further research is required to provide practical, cost-effective techniques and solutions.

The constant improvement of life quality is intimately tied to the improvement of environmental quality, among other things. This can be accomplished by implementing appropriate intelligent monitoring, analysis, forecasting, decision, and control systems based on intelligent tools and methodologies.

The focus of Environmental Quality Management is on resource recovery theories, applications, and social systems, as well as concentrated management for a sustainable future. These kinds of integrated intelligent systems are extremely important decision-support tools for managing environmental critical scenarios such as floods, severe air/water/soil pollution, earthquakes, tsunamis, storms, land sliding, avalanches, and others.

By detecting energy emission reductions, and CO2 removal, assisting in the development of greener transportation networks, monitoring deforestation, and anticipating extreme weather events, AI can expedite global efforts to safeguard the environment and conserve resources.

By maintaining an environmental quality system, we can:

• Keep a check on real-time analysis.
• Monitoring methane emissions.
• Tracking air quality.
• Observing environmental footprints.

Renewable energy generation continues to grow, partly due to increased investor interest; nevertheless, the scale of this shift is costly, and the sector must search for new ways to innovate to ensure this transition is cost, time, and success effective.

The technology has currently demonstrated its worth in the renewable energy sector by enhancing grid operations and optimization, demand-side management, coordination of distributed energy assets, forecasting of renewable energy, and materials innovation and discovery.

The post The Role of AI in the Development of Transportation, Energy & Environmental Quality Systems appeared first on AiThority.

Renowned statistician, Edwards Deming, poignantly noted, “Without data, you’re just another person with an opinion.” This stands true in the current times, especially when data is at the forefront of the much anticipated AI revolution.

Also, did you know that mistakes caused by insufficient data are substantially lower than errors caused by no data at all?  Data provides insights and in a way, it can be called that valuable commodity that has the power to even outlast systems. It’s like the scaffolding that can make businesses flourish and strengthen the process of urbanization and usher in the era of smart cities.

Today, smart cities are thriving and data and artificial intelligence have a lot to do with it. All thanks to the perfect concoction of technology and data, together, they are empowering cities to make the new urban phenomenon, a reality. A decade ago, nobody could have fathomed something like a big data revolution or an AI revolution. From sensor data and geospatial data to predictive maintenance and demand forecasting, smart cities are making the most of big data.

According to a PwC India survey, 71% of respondents are extremely positive about AI’s ability to help humans solve complex problems and live more fulfilled lives in the near future.

In this post, we are taking a look at the different forms of data cities are using for artificial intelligence.

How Data Is Consumed Is Smart Cities

Sensor Data

The information gathered by a Data Sensor component when it scans one or more IMS database environments at a particular moment in time and measures particular conditions (or states) present in those environments is known as sensor data. Sensor data also enables neighboring fixtures to interact with each other. In simple terms, sensor data is the output of a device that detects and reacts to some kind of input from the physical environment is called sensor data.

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Sensor Data in Smart Streetlighting

A smart streetlight is a type of public illumination that uses technology such as cameras, light-sensing photocells, and other sensors to provide real-time monitoring capabilities. Smart streetlight technology varies based on features and requirements, but it often comprises a combination of cameras and sensors.

Depending on the features and requirements, the technology behind smart streetlights can vary, but typically it entails a combination of cameras and sensors. Typically, smart streetlights include cameras, light-sensing photocells, and other sensors, to extract real-time data. These devices can detect movement and enable dynamic lighting and dimming when installed on regular streetlights. They help in citizen safety and significantly bring down power consumption.

How Sensor Data Works

Each city planner uses a specific technology, but here are some common features of smart street lighting.

• Lighting is controlled based on movement detection.
• Weather and environmental observation.
• Efficient parking management – keeping officers in the loop about accidents and parking regulations.
• Traffic management with the help of real-time data feeds. It helps to keep a check on speed, congestion, etc.
• Automatic emergency response in the case of accidents or crime.

Smart Meters and Disaggregation

In the last few years, smart meters have become more popular and are deployed at a much larger scale globally. A smart meter is a device that keeps track of data like electricity usage, voltage, current, and power factor. They transmit data to electricity suppliers for system monitoring and customer billing as well as to consumers for a better understanding of use patterns. Though they are mostly associated with electricity meters they can also refer to a device that measures water, natural gas, or heat consumption.

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If you have ever been wondering if you are paying too much electricity, then smart meters are the ideal solution. If you rely on this technology, it will mean that you will no longer have to depend on estimated bills. Also, they spare you from having to provide meter readings to your energy provider yourself because they automatically send the data over a digital network.

The Data Communications Company manages a national infrastructure that is used to transfer all smart meter data to energy providers (DCC).

The best energy tariffs now more often than not require you to have a smart meter, even though they don’t make energy cheaper on their own. Smart meters use a secure smart data network to securely and automatically deliver digital meter readings to energy providers at least once every month.

For energy suppliers and utility organizations, smart metering provides comprehensive data on residential or commercial energy consumption.

The ultimate goal of this technology is to sustainably reduce electricity consumption and costs by increasing knowledge about the electricity grids or smart grids’ current state, performance, and customer service standards.

In contrast to conventional meters, smart meters give users control over how much energy they use, enabling more general wiser energy consumption.

Features of smart meters

• Enhances the supply quality.
• Energy efficient.
• Better Network Planning.
• Reduced downtime.
• Consumption control.
• Transparency in data.

In essence, the conversion of the electrical grid into a smart grid is driven by the smart metre. The customer experience is being enhanced by the use of various digital technologies, such as edge computing, cloud computing, artificial intelligence, and big data, made possible by the data acquired from these devices.

Governments around the world have made implementing smart meters a primary priority. In the U.K., for example, there were 25.6 million operational smart meters in consumers’ homes as of June 2022.

Digital Twin Solution

Genesys’ 3D City Digital Twin Solution for Urban India was unveiled by Bentley Systems, Incorporated, a provider of infrastructure engineering software, and Genesys International, a mapping and geospatial content services company. Using OpenCities 365, Bentley’s infrastructure digital twin solution for cities and campuses, this will be the first city digital twin project to be launched by an Indian enterprise. Most of urban India will be covered by this extensive mapping and surveying effort, which has already started.

The reliable solution saved time and money by giving operators precise and current information on designing and erecting 5G towers, which eliminated the need for time-consuming tower inspections.

Sajid Malik, chairman and managing director at Genesys International stated that the company will be able to construct and curate city-scale digital twins utilising Bentley’s OpenCities 365 and Genesys 3D City Digital Twin Solution for Urban India, empowering government and corporate institutions throughout India to increase their execution, efficiency, and planning skills. He further added that this remarkable solution makes it possible to bring the assets from the field that are as-built into the office in a realistic model.

Malik iterated that they were impressed by Bentley’s digital cities portfolio’s technical prowess and consider it to be a critical differentiator that will actively enhance our current digital capabilities through sustained cooperation between Bentley and Genesys International.

Depending on what an end user requires, application and engineering data layers can be added once a 3D digital twin for each of the cities is complete.

Local governments will be able to enhance public services, such as urban governance, disaster management, emergency response, and tourism, thanks to these 3D city digital twins.

Depending on what an end user requires, application and engineering data layers can be added once a 3D digital twin for each of the cities is complete.

Also, it will assist governments in providing their residents with more resilient and sustainable surroundings through improved urban construction, upgraded utility and water networks that are optimised, location-based services, and other smart city efforts.

These 3D digital twins will be used to support and modernise operations in different private corporate verticals, including city gas distribution, autonomous navigation, e-commerce, telecommunications infrastructure, construction, renewable energy, and various other verticals.

Traffic Analysis

Urban Mobility is one of the most difficult problems. The demand for mobility will rise even more as urbanization and population growth add to the already insufficient mobility systems in many cities.

Analyzing traffic flows in an urban setting, examining similarities (or differences) between weekdays, and identifying daily peaks are the first stages of comprehending urban mobility.

Traffic analyses are carried out to compare weekdays, and weekends, and to find notable differences. Also, estimates of traffic flow on notable days, such as holidays or days with weather alerts, were examined to identify any trends.

Rewind to early 2000 when people would manually count cars at crossings, providing only a fleeting glimpse of the flow of traffic that was difficult to extrapolate to other traffic situations.

Road tubes made a difference in this situation by enabling 24-hour counting but offering little information on other crucial elements like vehicle classification, its direction, or traffic violations.

• Security cameras are undoubtedly a considerably superior option–they can watch 24/7 and provide a plethora of data. However, some estimates suggest that around 98% of this camera material goes unwatched and humans miss 95% of the crucial situations.
• Here’s where artificial intelligence significantly improves traffic management. For instance, AI-powered software can instantly evaluate traffic camera data and alert transport management systems. To gain a better understanding of traffic trends over time, it can also gather historical data.
• Cities may build real-time parking and traffic maps using CCTV cameras or road surface sensors built into parking spaces, saving drivers’ time by preventing them from having to wait for a place to open up or being stuck in traffic.
• The public sector is a part of smart transportation, and AI has made it possible to significantly improve public transportation. And now, cab firms like Uber are also utilizing AI to provide their consumers with improved ride experiences.

To cut logistical costs, time-sensitive cold chain or pharmaceutical use cases, ride-sharing incentives, or use cases like drivers nearing the end of their shift can all be targeted with the help of connected vehicle data.

AI and machine learning can also be used to construct precise, quantifiable hyperlocal network designs, allocate resources more efficiently, and map a given area in the context of its particular needs and multimodal demand.

• Formerly, Hangzhou was ranked as China’s fifth-most crowded city. Due to the citywide implementation of intelligent traffic technologies, it has now fallen outside of the top 50, according to TechWireAsia.
• As per the report published by The Brainy Insights, the global intelligent traffic management system market is expected to grow from USD 10.1 billion in 2021 to USD 31.07 billion by 2030, at a CAGR of 13.3% during the forecast period 2022-2030.

Mobility officials can react to emergencies more quickly because of improved traffic efficiency. These novel approaches should decrease travel time while increasing traffic control efficiency. The usage of public transportation more frequently can help cut down on air pollution and greenhouse gas emissions.

Intelligent traffic management systems offer accessibility, vehicle traffic, and road safety. Additionally, it provides real-time data for quick analysis and situational response. To reduce traffic congestion, numerous nations are attempting to enhance their traffic management systems.

Based on the fundamental idea of monitoring vehicles with radio frequency identification, intelligent traffic control systems may operate in real-time, increase traffic flow and safety, and are completely automated, saving money on ongoing, expensive human involvement.

Based on the fundamental idea of monitoring vehicles with radio frequency identification, intelligent traffic control systems may operate in real-time, increase traffic flow and safety, and are completely automated, saving money on ongoing, expensive human involvement. If this traffic control system is in place at every intersection in a city, the system’s greatest advantage will become apparent.

Recommended: AI-Based Image Recognition for Tolling and Traffic Management 

Cities have recently been growing exponentially as a result of modern lifestyles and various world economies. Urban expansion and sustainability goals of cities both rely heavily on communication and information technology.

Final Thoughts

The bottom line is data has extraordinary power. And when the data is complete and accurate, researchers, scientists, and decision-makers spend more time extracting valuable, real-time insights from it. Cities benefit from data derived from IoT devices and sensors to identify particular patterns and needs. The insight further help to minimize road accidents and traffic bottlenecks assist drivers in identifying a parking spot, enhance urban lighting, and refine the process of water and energy systems.

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Data is a key component to helping law enforcement and emergency services improve public safety, but there can be too much of a good thing.

Agencies today are inundated with data. The flood of incoming information from dispatch, records, alarms, alerts, social media and other internal and external systems can overwhelm and distract personnel or lead to inaccurate assessments of incidents.  Teams need help sifting through the data to uncover insights both during and after an emergency.

That’s where artificial intelligence (AI) comes in. Working behind the scenes, assistive AI can do the heavy lifting, making connections and recommendations faster than busy people can. Everyone, from dispatchers and first responders to crime analysts and city officials, can benefit.

Let’s explore a few scenarios.

Dispatchers and First Responders

During an incident, dispatchers and first responders need the right information to help them understand and manage a situation quickly and appropriately. That includes information to ensure the safety of responders.

AI embedded within dispatch systems can provide actionable insights during an emergency, detecting patterns from among the different 911 calls and making recommendations to dispatchers. This provides vital insights about emergencies as they unfold in real-time – from determining links among related calls and events to understanding a rapidly growing incident.

Real-Time Intelligence Centers

Real-time intelligence centers operate 24/7 and have a bird’s-eye view of incidents and information across a city or region, guiding resource deployment and investigations. While these centers combine multiple data sources and systems, not all data is relevant to the problems these agencies are trying to solve.

AI can put the ‘real-time’ in real-time intelligence, making it possible to flag anomalies and trends from among city-wide data and make proactive recommendations that teams can act upon.

Detectives and Crime Analysts

For detectives, having a unified view of crime can enhance their ability to solve crimes faster and more efficiently, while crime analysts need to mine data from multiple sources, identify patterns and analyze trends to help detectives and patrol divisions do their jobs effectively.

Much of this is done through database research of past events. However, an AI system could be set up to monitor data pertaining to specific locations or keywords (such as aliases or descriptions) and alert detectives and analysts when a new, relevant incident or report is logged. These proactive notifications can help police stay on top of events and make connections faster than before.

Multiple Agencies and Operations

While AI can play a major role at the tactical level of public safety, it can also help multiple city services collaborate to see the full picture of data and incidents happening in their jurisdictions.

In a city operations center, AI can mine not just public safety data, but also data from traffic management, public works, utilities and more, providing a comprehensive, real-time view of a city and alerting stakeholders of relevant issues. With this deeper connection across a city at all levels of operation, officials can better align services, resources and response.

The benefits of leveraging data to drive better decision making for public safety have long been clear. But how can we take advantage of this opportunity in a time of rapidly growing data from multiple sources delivered faster than ever before?

Assistive AI provides an answer. It gives agencies a second set of eyes, working alongside dispatchers, investigators, analysts and others. By making connections between data, it can help deliver greater insights for departments and better outcomes for the public.

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