In densely populated metropolitan areas, smart traffic tools can reduce travel times by up to 25%. While that's an extraordinary figure for one metric, it was a game-changer for someone living in an urban area where cars would crawl during rush hour, reducing travel time by 10%. That's a blessing. But 25%? That changes everything in the city from rushing down those two-lane avenues to trailing way behind in football traffic.
Regardless of whether you live in big cities or sold your soul to sprawl, you are cognizant that congestion and the traffic cycle are more of a problem; they are an ongoing nuisance affecting the quality of life, productivity, and overall health of commuters. The cycle of city traffic can generate anxiety for commuters and lead to dangerous situations behind the wheel. Some cities are gearing up for AI-based traffic controls for their cities. In this context, artificial intelligence (AI) employs Internet of Things (IoT) sensors and computer vision technologies to register vehicle movements, machine learning algorithms to develop a pattern for traffic, and data analytics to search for and solve the issues of traffic.
Traffic congestion affects more than just cars and pedestrians. Long periods spent sitting in traffic harm people's quality of life and mental health. Driving on congested freeways creates tension and anxiety for people. In 2023, urban commuters in the United States lost an estimated 43 hours a year sitting in traffic. And there is a financial cost for all that wasted work time. Beyond economic costs, environmental costs have become significant, with greenhouse gases and pollutants being the leading contributors to climate change and poor air quality. Carbon emissions from stationary cars in traffic represent an adverse effect on the planet and society.
Traffic accidents, without question, are the leading cause of death on the planet. According to a study performed by the WHO, automobile accidents lead to the deaths of nearly 1.19 million people every year. Intersections are perceived as hot spots for accidents, as cars, pedestrians, and bicycles have to navigate tight spaces. With old-fashioned traffic control and poorly planned infrastructure, prayer might be appropriate! As previously detailed, both manual intervention and fixed schedules need to be complemented by automated decisions in the operation of traffic management systems. Whereas traditional traffic management systems are no longer able to harness the dynamic quality of urban traffic, dramatic change and advanced capability, such as artificial intelligence, need to take the lead.
Almost every industry has improved with AI, and traffic administration will be no different. AI uses data to process traffic data using statistical analysis to study patterns and make recommendations for enhancements. If smart cities and other levels of government adopt AI traffic safety management systems that start using sensors, GPS, traffic detection, and traffic light signaling, the next generation will not know what it is to stand in traffic for long hours.
AI is changing the way traffic is managed, monitored, and controlled for movement in defense and border management applications. Advanced AI-enabled technologies that provide responsive, organized, and monitored movement solutions serving sensitive areas are transforming movement operations. The following are some of the primary technologies supporting AI traffic management systems:
Computer vision gives AI systems the ability to "see" and apply meaning to images collected from a surveillance camera or drone. In traffic management, computer vision is used to automatically count vehicles present, recognize license plates of vehicles, monitor congestion levels, and flag odd behaviours or circumstances. Along with vehicles like cars and buses, it is also important to ensure the safety of cyclists and pedestrians. Therefore, computer vision can be used to ensure traffic signals prioritize their safety. Within defense zones, computer vision can help identify unauthorized movements through a monitored area, assess traffic flows when accessing restricted zones, and provide command with real-time visual intelligence.
Machine learning (ML) algorithms assess historical data and currently gathered data to resolve trends such as peak traffic hours, congestion points, and make educated predictions. These systems will always learn about traffic behavior to improve routing, detect patterns, and anticipate risks. In a warfighting or first-response environment, ML can inform dynamic decision-making by predicting the most efficient, even the safest path, for transportation.
IoT sensors and radar systems are essential to make observations about the speed, volume of vehicles, environmental conditions, and movement patterns of vehicles. When used in conjunction with AI, these sensors will contribute a continuous stream of data in real-time, which increases situational awareness. For example, in border security, ground radar and embedded sensors can track incoming vehicles, and assess possible threats - all while operating even during low visibility or in remote areas.
Big data analytics means understanding trends and other emergent behavior, which means there is a desire to maximize the possibility of discovering and analyzing a dataset using AI-based traffic systems from the mass information coming from sensors, cameras, drones, and weather systems. This understanding is important so that the operator, for defense purposes, can make better decisions, react to incidents faster, and anticipate threats across borders and through military transportation networks based on needs.
AI-enabled traffic management is changing the way security, mobility, and efficiency is driven across critical infrastructure, including defense and border areas. These intelligent traffic management systems go beyond traffic control to real-time decision-making, intelligence-based planning, and proactively identifying and managing actions to eliminate potential disruptions. Here are a few applications of AI-enabled traffic management:
Dynamic traffic flow optimization utilizes AI systems that monitor the flow of traffic in real-time, utilizing cameras and sensors, and control traffic lights, traffic signal timing, and routing of vehicles to reduce congestion and maintain safe, dynamic flow. In areas of high security, this also provides the timely passage of military convoys, emergency vehicles, and commercial supply chains.
Automated vehicle recognition and monitoring have been identified as a strategic technology where AI systems can recognize license plates, vehicle types, and the driving behavior of drivers through computer vision (CV) and machine learning (ML). The use of CV systems can potentially identify suspicious vehicles, authorize or prohibit vehicles from entering an area, and manage checkpoint procedures without manual intervention.
If accidents, blockages, or threats arise, AI algorithms will rapidly suggest alternative routes. This is especially critical for military operations or crises that are time-sensitive.
AI can predict road wear and tear as well as wear and tear on devices such as traffic lights and surveillance systems. Predictive Maintenance leverages historical usage data along with environmental/usage conditions to help authorities make timely repairs with minimal downtime, consequently increasing safety.
AI systems can identify anomalies based on patterns of vehicle movement and behavior, such as loitering, erratic driving, or suspicious clustering of vehicles. Such anomaly detection lends itself to early threat detection in sensitive areas.
AI traffic management integrates with facial recognition, biometric scanners, and automated checkpoints to enhance and secure cross-border movement. It allows only authorized personnel and vehicles to cross the border securely, which enhances security and efficiency.
Traffic congestion challenges nearly every state, city, and metropolitan area. For nearly 99 percent of the traditional traffic control companies, traffic lights make up the bulk of their systems. When thinking about smart cities, it is ever-increasingly important to use AI and machine learning for traffic control. Shortly, AI algorithms will use an analysis of real-time inputs and past data to make predictions about traffic patterns that will allow traffic control to be proactive instead of reactive. Armed with the current state of the traffic environment, AI will also help to integrate many alternate forms of transportation and provide the best combinations of bicycle, walking, public transit, and ride sharing. Machine learning will also help shape efficient city designs by modeling scenarios on the best locations for roads and transportation corridors. Wouldn't it be cool for AI to help identify and then locate intelligent traffic signals throughout the city?
AI will also aid in the deployment of smart traffic signals throughout the city. Artificial Traffic Surveillance and Control (ATSAC), for instance, modifies signal timing due to variations in traffic conditions, using real-time detector loops at and between intersections. Due to vehicles communicating with city infrastructure through AI, we will have the ability to adjust traffic lights and routes in real time. AI will prioritize smart charging stations and the reduction of pollution as a means of managing traffic responsibly. AI will eventually be able to coordinate mixed traffic of both self-driving and human-driven cars. Predictive maintenance and dynamic routing will make public transportation more responsive.