The Role of AI in the Development of Transportation, Energy & Environmental Quality Systems

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.