Just as blood vessels carry vital nutrients throughout the body, roads, and traffic systems carry people, goods, and information throughout modern cities & countries. Their effective functioning is essential for the well-being of these societies.
Transportation is constantly influenced by external factors, including :
- Human behavior,
- economic fluctuations,
- seasonal changes.
To navigate this dynamic environment effectively, transportation companies are increasingly turning to artificial intelligence ( AI) as a powerful tool for decision-making. AI’s ability to analyze vast amounts of data from various sources enables transportation providers to anticipate potential disruptions, optimize resource allocation, and enhance overall system efficiency.
The UITP International Association of Public Transport recently reported that 86% of public transportation companies are actively engaged in partnerships to develop and implement AI-driven solutions. This widespread adoption reflects the industry’s recognition of AI’s potential to address the multifaceted challenges faced by transportation systems and pave the way for a more sustainable, efficient, and safe transportation future.
AI in transportation has many practical uses, especially through vision services like spotting objects or tracking them. While the flashy AI applications in transportation, such as self-driving cars and smart highways, get a lot of attention, there are other less flashy but highly useful applications.
For instance, Ai keeps an eye on intersections and paths used by pedestrians and cyclists to detect accidents and enhance safety.
Additionally, AI in transportation studies traffic patterns to figure out why there are delays or traffic jams.
Modern driver assistance features, such as self-parking, lane recognition, and adaptive cruise control, are now standard in many new vehicles. While some, like Hyundai’s advanced cruise control, have gained widespread adoption, it’s essential to note that they don’t fully replace the driver’s role.
Although autonomous vehicles are a reality, achieving full self-driving capability is a complex task that demands extensive effort and a substantial amount of data. Any discrepancies in real-time data transmission and processing can lead to severe consequences, making it challenging to deploy these projects universally.
In Tokyo, autonomous taxis are set to operate on selected roads in spring 2023. However, major Japanese automakers like Toyota and Nissan haven’t committed to a specific timeline for incorporating AI into their self-driving vehicles. Despite significant resources, advances in sensors and software are still pending.
In the United States, Waymo initiated self-driving vehicle development in 2010 and began testing trucks and minivans on public roads in 2018, but mass production is yet to commence.
Ensuring the safety of passengers in autonomous vehicles is a time-consuming process. Yet, as technology advances, self-driving cars will become more dependable and widespread. The integration of sensors, cameras, and GPS in conjunction with artificial intelligence is poised to significantly impact public transportation.
AI technology has the potential to reduce human error rates and enforce compliance with safety regulations, thereby mitigating driving risks.
Nighttime driving poses a considerable challenge for many motorists. The capability of computer systems to automatically identify pedestrians in images and videos presents a promising solution through AI-powered cars. This technology not only enhances safety but also paves the way for features like auto-pilots and, in the future, fully autonomous vehicles.
These advancements could potentially enable drivers to engage in activities such as sleeping or chatting without risking traffic accidents.
Pedestrian detection, a focal point for Computer Vision and Pattern Recognition, presents unique challenges due to the unpredictable behavior of pedestrians, resulting in a multitude of edge cases in the data.
The unpredictability introduces complexities in behavior prediction, posing a significant hurdle to the success of self-driving cars.
Beyond behavioral challenges, numerous obstacles in training data must be addressed. Factors such as varying lighting conditions and the diverse poses or clothing choices of pedestrians contribute to the complexity.
To surmount these challenges, AI systems require extensive training data, a process that necessitates a significant investment of time.
To optimize traffic flow, the implementation of artificial intelligence in traffic management is crucial for creating smarter and more environmentally friendly roads. Imagine a world where unnecessary stops and starts become a thing of the past!
Utilizing computer vision machine learning, AI processes manages, and refines extensive data gathered from a network of sensors and cameras installed on roadways. AI and big data systems meticulously analyze this information to unveil intricate traffic patterns.
The resulting insights serve as valuable input for smart systems, enabling accurate traffic predictions and the identification of potential road blockages. Artificial intelligence, armed with this knowledge, proactively recognizes and forecasts issues that could lead to congestion.
The significance of traffic signaling and intelligent transport systems technology in ensuring road safety cannot be overstated. The timing and configuration of traffic lights play a pivotal role, exemplified by adjustments like extending pedestrian intervals to allow for a “walk” signal several seconds before turning traffic receives a green light.
This not only improves pedestrian safety by providing a head start but also enhances visibility, reducing the risk of accidents. Siemens Mobility has introduced an innovative AI-powered system that monitors traffic through cameras, dynamically adjusting traffic light patterns based on real-time road situations to minimize congestion.
Innovative AI solutions extend to intelligent traffic surveillance and control systems, overseeing speed management, issuing lane departure warnings, and facilitating information exchange with urban traffic control systems. Today’s vehicles interact with each other and the road infrastructure through Cooperative Intelligent Transport Systems (C-ITS). This interaction allows the sharing of data with traffic managers, utilizing vehicle-to-vehicle and vehicle-to-infrastructure communication channels.
These channels serve various purposes, including emergency braking warnings, distance sensing, improper-driving detection, collision-avoidance systems, weather-related skid warnings, and optimized intersection management. The result is a comprehensive and interconnected system contributing to a safer and more efficient traffic environment.
Transportation delays, especially in air travel, pose a significant challenge, potentially costing up to $32.9 billion in the USA alone, according to UC Berkeley. AI implementation offers a solution, predicting short-term effects such as weather or technical issues that may cause delays. By processing real-time aircraft data, historical records, and weather information, AI powered by machine learning reveals patterns, providing valuable insights for the air transport industry to mitigate delays.
Google Flights utilizes AI to predict delays and computer vision systems can monitor vehicles, predicting delays in a deployable and maintainable manner.
Videos offer clear insights, unlike other numerical data collection technologies. Additionally, AI, trained with historical and real-time data, refines arrival time predictions. For instance, Uber’s routing engine combines real-time traffic measurements and map data to predict ETAs, further refined by machine learning for accurate outcomes.
Monitoring road conditions is crucial, especially considering that pothole damage costs US drivers over $3 billion annually. Despite public pressure on governments, potholes persist, seemingly appearing overnight.
Transportation AI, powered by computer vision, offers a solution. This technology can successfully identify road defects and assess infrastructure changes by analyzing the road surface.
Computer vision algorithms not only identify the extent of road damage but also alert authorities for prompt maintenance. These algorithms process image or video data to automatically detect and classify cracks, paving the way for targeted rehabilitation and preventative maintenance without human intervention.
Automated Pavement Distress (PD) detection enhances road maintenance efficiency, improves safety, and provides real-time updates for quicker repairs, ultimately saving time and money. EyeVi, for instance, utilizes computer vision and machine learning to address road surface damage in transportation.
Computer vision-powered parking management is poised to revolutionize the tedious task of finding parking spaces, a struggle even acknowledged by Nobel laureates who humorously lament, “The universe is expanding, but where do I park?”
The quest for parking, often stressful and environmentally unfriendly, contributes to traffic congestion.
To address this challenge, parking lots equipped with sensors measuring car distances can monitor available spots. However, to enhance this system, cameras, parking meters, and computer vision step in. Automatic number plate recognition by artificial intelligence identifies parked vehicles and tracks their prepaid parking time.
This data is then utilized to dynamically update a real-time map showcasing all empty and soon-to-be-available parking slots. Drivers can conveniently access this map on their mobile devices, enabling them to swiftly locate vacant spaces with low occupancy levels and save valuable time.
Traffic incident detection stands out as one of the most extensively researched domains within AI transportation, driven by the primary goal of minimizing disruptions to traffic flows.
Historically, video surveillance served as a potent tool for monitoring road networks and intersections in real time. However, human limitations hindered its effectiveness, as one person couldn’t simultaneously monitor multiple cameras with equal efficiency, leading to crucial incidents escaping notice.
Automatic incident detection, empowered by computer vision, addresses this gap. Constantly monitoring all cameras, it searches for incidents, queues, and unusual traffic conditions. Motorola Solutions, for example, offers an AI-enabled user interface to ensure swift responses to critical events, and AI in transportation can even predict impending traffic difficulties.
Collaborative efforts, such as MindTitan with the Estonian Road Administration, produced a traffic accident prediction model. Utilizing data on offenses, accidents, weather conditions, and police patrol details, this model forecasts the risk, severity, and root cause of traffic accidents, contributing to improved road safety.
In Bellevue (WA), a noteworthy AI project utilized over 5,000 hours of video footage to identify predictors of crashes at intersections. Processing data from high-definition traffic cameras, the AI model pinpoints traffic hot spots by analyzing factors such as traffic volumes, speeds, and near-crash indicators.
Another application of AI in transportation is in law enforcement. Intelligent systems can detect dangerous driving behaviors, such as texting or drinking, alerting nearby officers to intervene and prevent accidents before they occur. Furthermore, these systems can predict optimal patrol placements, as demonstrated by MindTitan’s work with the Estonian Police and Border Guard Board, forecasting emergency calls based on historical data and environmental factors.
Automated license plate recognition employs computer vision systems analyzing highway and street camera video to detect license plate numbers, marking location, date, and time.
A central server processes these images, utilizing optical character recognition (OCR) to identify digits and letters.
Primarily used by the police for vehicle location, it quickly determines if a car was at a crime scene.
Additionally, it aids traffic, parking, and toll management by identifying travel patterns.
Despite its utility, automated license plate recognition is contentious due to privacy concerns. Critics argue it may reveal private information about a driver’s life, urging cautious use of this technology.
In 2020, the USA recorded 633 deaths from drowsy-driving-related crashes, highlighting a critical issue, as acknowledged by the UK government. Despite emphasizing personal responsibility, statistics reveal the inadequacy of relying solely on driver awareness.
To address this, companies deploy computer vision in vehicles, enhancing monitoring with video equipment in car cabins. This technology employs face detection and pose estimation to recognize signs of drowsiness and emotional states, providing real-time alerts to drivers. By urging them to pull over and rest, AI has the potential to prevent numerous crashes and fatalities, ensuring safer roads for all travelers.
The same system issues alerts for driver distractions, intervening when disruptions, like mobile device use, occur. It effectively detects other distractions, such as conversations with backseat passengers, safeguarding driver focus on the road.
AI has greatly impacted the transportation industry and is a key component of smart cities, which aim to use digital technology and shared knowledge to improve public well-being and productivity.
The list of benefits is already extensive, yet AI is still expanding, and the benefits will be too.
1. Reducing labor costs
By automating repetitive and time-consuming tasks, AI allows human workers to focus on more strategic and value-added activities. This, in turn, reduces labor costs and increases profits.
2. Improving safety
AI’s capabilities extend to monitoring and predicting traffic accidents, preventing incidents, and enhancing traffic flow. In high-risk industries such as manufacturing and energy, it can also contribute to improving overall safety.
3. Streamlining logistics
Optimizing transport routes, minimizing waiting times, and enhancing stock management are areas where AI can be applied to businesses. This optimization leads to increased efficiency and profitability.
4. Improve traffic management
AI’s predictive abilities extend to traffic congestion, enabling cities and transport authorities to take proactive measures to alleviate potential issues.
The integration of AI into transportation has brought about numerous benefits, ranging from improving traffic management and safety to reducing carbon emissions. Thanks to its ability to process vast amounts of data and automate tasks, such as continuous monitoring of traffic, it provides road users with exceptional attention to prevent problems and enhance safety.
In aviation, AI-driven systems can predict patterns in traffic data, such as weather conditions and delays, to improve revenue through intelligent fleet management.
However, given the complexity of the transportation sector, it is essential to focus on specific challenges and work with a team of machine learning experts to develop effective solutions.