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AI Applications in Transportation

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Artificial intelligence (AI) and machine learning (ML) have far surpassed the science fiction stories of decades past. As panic over the robots coming for our jobs subsides, it has become increasingly clear that AI and ML developments are perfectly positioned to help humans do their jobs better, not replace us entirely. Armed with AI tools, human experts can retain control of their workflows and thoroughly oversee tech intervention to mitigate the risk that it will wreak havoc on critical infrastructure.

In the transportation space, there’s virtually no area left untouched by these tech innovations. The market for artificial intelligence in transportation grew from $2.6 billion in 2022 to $3.1 billion in 2023, representing 19.4% compound annual growth (CAGR). That upward trend is only expected to accelerate in the coming years; if this growth continues, the market is expected to reach an estimated value of $6.3 billion by the year 2027. 

Infrastructure investment has been a hot topic in the U.S. since the Biden administration’s $1 trillion infrastructure bill became law in 2021, and AI has emerged as a key to modernizing many of the nation’s — and the world’s — roadways, skyways, and seaways. In this article, we’ll look at seven ways artificial intelligence is already improving transportation infrastructure around the world today and imagine future applications where the technology may be headed.

Autonomous Vehicles

One of the most obvious applications of AI in the transportation space is in the development of autonomous vehicles. Self-driving cars have been a futuristic fantasy for decades, but advances in AI technology have put them realistically within reach. The many cameras and sensors that make autonomous vehicles possible require AI, machine learning, and computer vision technologies to make sense of the huge quantities of data they produce. When they do become the accepted norm, autonomous vehicles promise to improve productivity and efficiency, greatly reduce the risk of human error on the road, and optimize travel by aligning with traffic systems that are, of course, also powered by AI.

Self-driving taxis were launched in Tokyo ahead of the 2020 Olympics, thanks to a collaboration between ZMP and Japanese taxi company Hinomaru Kotsu. Honda is expected to officially enter the space with its own autonomous cab service by 2026. About 200 self-driving taxis are already ferrying passengers across Beijing, Guangzhou, and Shenzhen, China, thanks to a partnership between Toyota and In the United States, Waymo — previously known as the Google Self-Driving Car Project — formally introduced its driverless taxi service in 2019.

It’s important to note that in many driverless vehicle applications today, a human driver is still required for safety reasons. Until the technology is sufficiently capable of functioning truly autonomously and without moment-by-moment human supervision, it’s likely we’ll continue to see more of this hybrid approach. And while early ideas of driverless cars scrubbed human patterns in favor of total robotics, today’s autonomous vehicles are tasked with eliminating risk and error while still being able to mimic and anticipate decidedly human behaviors on the road.

Advanced Driver Assistance

Even though roads and highways aren’t yet dominated by fully autonomous vehicles, artificial intelligence is already in place in many modern cars. Advanced driver assistance systems (ADAS) use a combination of AI-backed sensors and cameras to identify other vehicles, pedestrians, or objects on the road, in addition to analyzing driver facial expressions inside the car. Then the ADAS can either issue alerts when danger is present or execute specific autonomous actions that prioritize safety. 

Popular ADAS can avoid collisions, help with parking or complete the process entirely, and even take over if a human driver loses control of their car during inclement weather or an accident. ADAS from companies like Honda, for example, commonly use AI to power collision mitigation and automatic braking, adaptive cruise control, and lane keeping assist systems, while Maserati’s ADAS offers highway assist, blind spot assist, and hill descent control systems. Subaru’s driver monitoring system uses facial recognition to automatically restore the driver’s settings and configurations and issue drowsiness and distraction warnings.

Fleet & Cargo Optimization

While much of the self-driving car frenzy has been focused on personal vehicles and taxi and ride-share applications, there are also AI applications in the trucking space. Fully autonomous cargo truck operations would reduce their costs by as much as 45%, saving the for-hire trucking industry between $85 and $125 billion, according to McKinsey. That’s because AI would take over coordinating the complex movements of multiple trucks in a unified caravan, enabling them to advance, brake, or even reroute almost simultaneously.

That advanced system is a step beyond more baseline developments, like deploying fleets of trucks along the most efficient routes in response to registered weather patterns, traffic congestion, emergencies and accidents, or other road blockages. That technology is already quite common; Anheuser-Busch reduced late deliveries by 80% using their AI-powered Wise Systems program, for example.

These concepts apply to the air and sea as much as to driving infrastructure; Aimee is an AI program helping increase air traffic controller efficiency at Heathrow Airport with a stated goal of expanding flight capacity by 20%. It analyzes traffic data from the tarmac as well as atmospheric data and weather patterns in London’s infamously temperamental skies. The Port of Rotterdam, which sees as many as 30,000 port calls each year, uses an AI system to optimize ingoing and outgoing ship traffic. So far, they have reduced wait times by 20%, saving ships both time and fuel.

Traffic Management

AI roads go hand-in-hand with AI vehicles. Using cameras and sensors affixed to existing traffic infrastructure, municipalities can use AI technology to conduct more effective analysis of traffic flows. Improving traffic management also improves road safety, not to mention decreasing congestion and lessening the impacts of climate change. Computer vision techniques can help identify accidents and blockages in real time, redirecting traffic away from congestion points and informing GPS navigation systems of the shortest and most efficient route to a given destination.

When road repair or emergency response teams are necessary, AI and machine learning tools can make sense of all that traffic data quickly, empowering human managers to dispatch the appropriate professionals to the scene. AI also powers the road transportation systems that most of us interact with every day; decisions about traffic light switching can be automated based on vehicle density and traffic patterns that the sensors pick up across the grid. 

Many early-stage smart city systems have started their implementations with simple traffic management. Pittsburgh’s SurTrac model cut travel times by 25% and limited emissions by reducing the time vehicles spent idling. And while there are a few established players in the traffic management space, plenty of startups are coming on the scene to introduce new technologies. Companies like AIWaysion and Flow Labs promise to create robust and reliable traffic models, leveraging AI to derive more value from the huge stores of transportation data that are available in today’s digital world. But as they get off the ground, those startups are often hyperfocused on quite narrow applications of traffic management tech.

Smart Parking

Artificial intelligence stands to improve the often painful experience of parking from many angles at the same time. First of all, smart parking systems can feed data from cameras and sensors city-wide (or within specific parking structures) into AI algorithms to identify available parking spots, making it easier for drivers to plan their routes. Beyond improving drivers’ experiences on the road, this also helps reduce time spent driving around in circles while searching for a place to park, further reducing unnecessary carbon emissions.

This is an example of AI technology that requires a specific interface, which is where AI apps come into play. Each app is designed to serve up the raw data and algorithmic conclusions in an easily digestible format that caters directly to the drivers. For drivers looking for local parking spots, there might be an app that shows a map or list of available spots in real time. Siemens’ Smart Parking program, for example, uses radar sensors to detect available parking spots and display their locations in existing GPS navigation apps.

Traffic Rule Enforcement

On the enforcement side of the equation, AI can also help law enforcement officers more easily detect parking violations and other regulatory breaches. From parking at expired meters to driving over the speed limit, identifying violations more quickly helps make roads safer and makes it easier for officers to penalize serious offenses. An AI app interface in this case might mimic a traditional dispatch system, helping law enforcement agents triage violations by severity or geographic proximity. 

AI tools can also power automated license plate recognition (ALPR) systems, which streamline the process of identifying and penalizing traffic violators. Computer vision systems paired with road-mounted cameras can automatically details like car make and model and even read license plate numbers, sending notices, tickets, or fines to the address where a driver is registered. When law enforcement agencies are investigating broader or more serious crimes, ALPR can help determine whether a specific vehicle was present at the scene.

ALPR’s applications reach beyond law enforcement too. The ability to automatically read and register detailed information about a car and its plates makes it easy to automate tolls, tickets, and fares, run parking management systems, or track assets belonging to a car dealership or car rental company, for example.

Maintenance & Repairs

One of the most promising benefits of AI-based transportation systems is that they can often self-diagnose when something goes wrong. Whether hardware- or software-based, most systems degrade over time. With AI and machine learning algorithms in place, many of the applications we’ve covered here can predict their own demise before it happens so that technicians can implement repairs before problems set in.

This is possible because in addition to whatever data a given camera, sensor, or system is designed to detect and process, it’s also capable of recording data about its own performance. AI systems can be trained to detect ranges of performance from optimal to normal to subpar, so that when something is missing, broken, or just not performing correctly, they automatically alert a human supervisor who can take action. 

Then supervisors and technicians can use those AI-backed insights to get directly to the problem and speed up repairs instead of rooting around trying to find the problem without detailed data. This type of predictive monitoring can have major consequences; it works to prevent vehicle failure ranging from unmanned drones to commercial aircraft, avoids system shutdowns, and ensures uninterrupted service to power grids, traffic systems, railway lines, and more.

Implementation and Project Management

While there are myriad applications of AI in the technology itself, AI can also help streamline and elevate the implementation of new technology and software by automating mundane tasks, allowing project managers to concentrate on complex issues that require human judgment. It can predict project trajectories and risks, offering solutions by processing extensive data. The seamless integration of AI ensures that project management not only keeps pace with technological advancements but also becomes a cornerstone in the successful and expedited delivery of innovative solutions.

Overall, artificial intelligence has already had a marked impact on transportation infrastructure around the world. The more these systems deliver measurable results, the easier it will be to secure much needed financial investment, not to mention emotional buy-in from the public. When transportation companies and government providers can clearly demonstrate that AI is leading to reliable cost reductions, sustainable solutions, increased safety, and improvements to everyday life, that will be a clear sign that the future of AI is here. 

Author: Beacon