Introduction: AI in traffic management
There has been an observation over the years that the traffic in most of the cities across the world has been getting worse and worse over time. Traffic is a major global problem without a doubt. In the US for example – drivers spend up to 58% more time stuck in traffic compared to drivers in all other cities in the world, primarily because traffic congestion is the main obstacle.
The use of AI in traffic management is poised to reshape urban transportation, relieving the bottlenecks that snarl traffic in cities. As a result, we will reduce not only traffic congestion and travel times, but also emissions (by reducing the time we spend in traffic).
With the rapid advancement of artificial intelligence (AI), road traffic management has changed dramatically. AI now predicts and controls the flow of people, objects, vehicles, and goods at different points on the transportation network very accurately. By optimizing traffic flows across intersections and improving safety during periods when roads are closed due to construction or other events, artificial intelligence is providing better service to citizens than ever before, as well as reducing accidents. AI’s ability to process and analyze vast amounts of data has also enabled effective mass transit, such as ride-sharing services. But how is AI transforming road traffic management?
How Can AI Help In Traffic Management
As part of traffic management, artificial intelligence analyzes real-time data gathered from cars, buses, and trains to identify patterns that may indicate safety risks. AI analyzes the realtime data to identify patterns that may indicate safety challenges and helps us de-risk them. With coordinated traffic light information, urban planners can suggest ways to reduce the number of accidents and mitigate these risks.
Coordinated Traffic Lights – Traffic signal Management
Smart coordinated traffic lights help us digitize road traffic data and relay it in realtime. They help organize traffic and keep it flowing.
It was once the responsibility of humans to operate traffic lights. They used timers and other tools to maintain order. Now, however, traffic lights are controlled by computers. The purpose of this change is to make things more efficient. It allows traffic lights to be timed more accurately.
The goal of making traffic lights smarter has been to increase the efficiency of drivers in recent years. This will help in recognizing patterns in traffic to suggest alternatives, better urban design models, better fuel efficiency, better battery usage for EV’s shortening distances, and improve efficiencies along with sustainability.
Also Read: Artificial Intelligence and Urban Design
Automatic Distance Recognition
In automatic distance recognition (ADR), sensors such as lasers, radars, and cameras are used to detect the distance between a car and the objects around it. This is one of the principles used in self driving vehicles.
ADR’s help prevent accidents as this principle can be applied to trigger automated breaking, decide speed, and avoid domino effect of slowing traffic. This will in turn help us design better roads, and infrastructure.
AI helps identify the potential congested areas ahead of time, enabling drivers to avoid traffic jams and save time during events such as concerts or other big events. This will help event planners to work with city officials to identify additional, flexible parking options in the area. This ability to adapt to expanding situations in realtime gives the urban planners provide smarter data driven choices and help gives every one a better experience.
Better Route Planning
AI driven route planning across towns and countries can help us identify and use efficient, sustainable and economically viable routes to help us get to our destinations. This in long term will help us improve supply chain management with complex distribution challenges. It can also help us improve emergency management in smart cities.
Better Law Enforcement via AI
With ITMS, offenders are automatically fined as per the law of the land, supported by evidence data in the form of snapshots & videos, as part of traffic management. Using AI to detect speed violations, the user is notified when multiple people are riding a bike or motorcycle without helmets. This prevents accidents involving those two modes of transportation and other motorized vehicles. A holistic solution to the current traffic menace can also be achieved by integrating the system with CCTV and Traffic Control systems.
Is it possible to manage traffic intelligently?
An applied field of computer vision, ITS is concerned with classification of vehicles, detecting traffic violations, and analyzing traffic flows. By keeping track of factors such as the distance between moving vehicles using ADR’s, traffic volume, PCR ratio, and pedestrians at crossroads, ITS helps reduce congestion.
IoT and AI are used in ITS to improve mobility, reduce pollution, and reduce death rates by implementing smarter solutions incorporating AI and IoT. the bigger issue Intelligent traffic systems can solve is congestion. Such congestion leads to higher fuel consumption, increased air pollution, unnecessarily wasted time & energy, chronic stress & other physical health problems. Likewise, higher traffic violation rates result in an increased number of road deaths. As a matter of fact, violations such as Red Light Violation, Over-speeding, not wearing a seatbelt or helmet while riding, are some of the most dangerous driving behaviors and are responsible for a large number of traffic fatalities.
Constructing roads, bridges, underpasses, and creating mass public transportation systems are not enough to reduce traffic congestion and management, which is becoming increasingly difficult as time goes by. Hence, the introduction of a modern & intelligent system for resolving these issues is essential.
As a result of the use of Artificial Intelligence (AI) within Intelligent Traffic Management (ITMS), many of these issues related to traffic law enforcement can be addressed and solved using AI. Through this process, commuters can enjoy a smoother, more convenient, and safer commute by improving road traffic discipline. A system of this sort can not only help law enforcement agencies to penalize the offenders but can also act as a deterrent as well.
An Intelligent Traffic Management System (ITMS) combines artificial intelligence with cameras installed at traffic intersections in order to detect and identify vehicles disobeying traffic rules and generate real-time alerts at the Central Command Center. Fines can be automatically imposed on offenders according to law and can be sent electronically to violators.
The fines are accompanied by supporting evidence in the form of pictures and videos. It can also monitor the flow and pace of traffic movement to provide real-time traffic management.
Additionally, an adaptive traffic-control system can adjust traffic signal cycles in real time in response to changing traffic conditions based on information received from these cameras. The ATCS system will almost half the waiting time at traffic signals once it’s fully operational. In addition, the Traffic Command & Control Center helps traffic authorities understand ground situations and trends across the city like never before, enabling them to plan and take preemptive actions.
Combining new communication technologies with artificial intelligence (AI) could also help us deal with our clogged roads, so they can cope with the growing number of cars, by crunching vast amounts of data in real-time.
An ITMS system can work in conjunction with the existing CCTV and traffic control system to provide a holistic solution to the current traffic menace. Not only that, but various AI-based traffic analytics can be deployed over existing traffic enforcement and surveillance systems as well, with minor tweaks and adjustments, resulting in a highly optimized solution at a lower cost to the public purse.
A modern Intelligent & Integrated Traffic Management System would be a great idea for not only improving traffic flow on urban roads & reducing commuters’ mental agony, but also saving lives from road accidents.
How does AI benefit traffic management?
The road traffic process can be significantly improved in many ways. In spite of the fact that there is no apparent reason why drivers have to wait at traffic lights for minutes on end – except that traffic lights are programmed to work according to a fixed pattern, independent of traffic conditions at the moment – all drivers can relate. Artificial intelligence can be used to keep traffic moving based on the current situation for a number of reasons:
- Traffic that is flowing, with no traffic jams or domino effects, is good for the environment. Using AI and making smart decisions improving efficiencies can help us deeply integrate sustainable strategies in traffic management.
- It helps improve quality of air by reducing air pollution.
- It enables the optimization of supply chain management, such as last mile deliveries, which is of great benefit to the economy.
- The most frequent cause of accidents is human error, which could be largely reduced with the help of better road traffic management and the use of autonomous vehicles.
- Truck Platooning is the process of electronically connecting several trucks that drive in convoy on the highway. Think of it like flying swarm of drones. Only the leading truck is manned by a human driver. The following trucks are controlled by AI.
All of these factors contribute to optimizing the entire traffic system, benefitting everybody.
It helps transportation managers plan routes more effectively by displaying upcoming events on an easy-to-use visual map that can increase ridership and service levels.
AI in road traffic – Quality Data
Artificial intelligence traffic management systems will be implemented as part of the infrastructure for self-driving cars.High-quality data is essential for these systems to function correctly and maintain road safety.
In addition to the programming of the algorithms, a large part of the quality of the software designed for road traffic management is determined by the amount and quality of the training data used. It is more likely that road traffic design will be safer if machine learning datasets are more reliable and realistic driven by realtime ground data.
For a conclusion on the contribution of autonomous driving to road safety and the reduction of accident figures, a reliable comparison must be made:
- How many accidents are caused by AI driven by bad datasets?
- Is it common for human error to cause accidents in similar circumstances?
Using AI to manage traffic – A complex conversation
In traffic management, artificial intelligence (AI) is causing a complex debate. Some believe that AI can reduce congestion, improve fuel efficiency, integrate sustainable strategies in traffic management, while some raise a vital point of privacy and tracking of vehicles that can lead to privacy invasion.
As part of traffic management, AI can be applied in many different ways. For example, emergency vehicles can bypass traffic lights or other obstacles when responding to an emergency by using emergency vehicle exemption.
By prioritizing public transportation at critical intersections, transit signal priority improves overall travel times for passengers.IoT based pedestrian safety systems are able to change crossing signals more quickly as soon as they cross the street.
AI Traffic Systems For A Smart City
It refers to a city that utilizes technology efficiently to provide citizens with services and benefits. These cities would be built using cutting-edge technology to offer services such as public transportation, traffic monitoring, and waste management to the government and citizens.
ITMS also contributes to smart city goals such as environmental sustainability besides improving traffic conditions. Several methods are available to improve traffic behavior & avoid congestion in a city such as Automated Number Plate Reading, Red Light Violation Detection, Speed Violation Detection, Triple Riding & No Helmet Detection, Free Left Turn Obstruction, Wrong-Way Driving, No Seat Belt & Mobile phone usage while driving, Hot listed vehicle detection, Traffic Flow & Congestion detection.
Smart cities have a number of characteristics and features, including the following.
Adaptive traffic control system (ATCS)
ATCS, or adaptive road traffic control system, uses artificial intelligence (AI) to optimize the flow of vehicles in an urban environment. Therefore, city authorities will be able to better understand traffic patterns and ground conditions and reduce traffic light waiting times by up to half.
The market for smart traffic or intelligent traffic systems (ITS) is growing rapidly. ATCS plays a significant role in this industry.
We are seeing a growing number of automated vehicles on our roads. While most people associate automated vehicles with self-driving cars, this is just one kind of automated vehicle.
- Delivery vans or drones
- Commercial aircraft
There are a number of advantages associated with automated vehicles compared to human manned vehicles, such as:
- Reduce idling costs, improving fuel efficiency, and integrating sustainable strategies to reduce pollution.
- They can improve and automate parking processes, enabling productivity increases.
- Improving road safety by using automated systems and implementing process that have better checks and balances.
Intelligent parking planning
Using realtime data and building apps that specialize in predictive analytics and event forecasting will help the cities create intelligent parking planning and strategies. These applications can predict traffic congestion and blocked roads, to parking availability. This is a great service for the residents of smart cities as it saves a lot of time and fuel, battery time while users find parking, this is a huge improvement of quality of life for the users and can be a good source of revenue for the city.
Improved Traffic Flow Reduces Traffic Congestion.
By routing cars around congested areas, optimizing delivery routes, and reducing construction costs, artificial intelligence can reduce traffic congestion. Pedestrians, cyclists, and cars can be automatically identified by smart cameras at intersections to enable smoother flow of traffic. Air quality or school traffic are two examples of where traffic management systems must be adapted to meet the needs of users.
The overall traffic flow in the city is currently still managed by humans, even if many tasks can be automated, such as accident reporting and traffic rerouting to improve efficiencies and quality of life for users. With the advent of artificial general intelligence (AGI), these tasks can also be automated.
AI Powered Cable Car System
AI powered cable car system can be used to improve congestion on the roads, by allowing people to use data driven cable cars that are flexible and can run on sustainable energy.
Is this the future of urban #transportation?
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Safety and Emergency Situations
All citizens are concerned about emergency situations. In emergencies, it is important that the authorities are able to react quickly and efficiently to evolving situations to make sure everyone’s safe. This can be accomplished by implementing an Integrated Traffic Management System (ITMS), which will automatically regulate signal lights and warn motorists about challenges and improve traffic flow. The ITMS can help identify better routes and reduce congestion on main routes for faster transition and travel for emergency vehicles.
Transit Planning – Intelligent Transportation Systems
Using AI to plan public transit can help reduce travel times and traffic congestion drastically while increasing the effectiveness of buses, trains, and ferries. Using AI, planners can determine which type of transportation works best and which route is the most efficient for a particular area.AI can also help create better schedules for transit authorities.
Planned development and use of land, transportation systems, public facilities, and services such as water supply, waste management, and energy distribution are all included in urban planning.
Planning for urban development involves balancing the needs of different groups such as local residents, businesses, commuters, and tourists in order to create a livable environment.
For cities to plan effectively, they need accurate statistics on population size, demographics, employment trends, economic conditions, etc. They also must understand how people move between neighborhoods.
Artificial intelligence and machine learning can help cities collect real-time data more efficiently and analyze it faster to identify critical patterns. This helps planners allocate resources more effectively.
Also Read: What are smart cities?
AI-Assisted Traffic Management Challenges
In terms of traffic management, AI presents several challenges.
- Data acquisition and understanding the complexity of the data.
- Data processing and feature extraction for predictive modeling.
- Model deployment, monitoring, and upgrades.
- Feedback analysis and learning from errors.
- Dealing with uncertainty and noise in the data.
Integrating different types of data (e.g., video, image, GPS)
- Is the system scalable as cities grow and become more complex?
- Privacy challenges.
- Process Standardization across multiple municipalities.
Cyber Security Challenges
Today, cyber security is a very important issue that has a significant impact modern day technologies that we depend on. In light of the fact that systems such as AI based traffic management, which are primarily concerned with managing road traffic, are at risk of cyberattacks from hackers who are capable of causing significant damage, making cybersecurity an essential part of the tech ops.
Road traffic management systems can be vulnerable to cyber attacks because of computer-based components, such as GPS, mobile apps, and websites, or interconnected systems with multiple sources of vulnerabilities which can disrupt traffic flow or cause severe accidents.
As a result of AI based traffic management systems, a city’s transportation department will save time and money, and it will have a smaller environmental impact while drastically improving efficiencies. It also helps to improve quality of life of the users which is a huge positive for the economic spend on such systems.
It is important to consider whether the long-term cost-effectiveness of the autonomous vehicles will actually be beneficial to the governments, systems, and users. Constant evolution in technologies in autonomous vehicles, and traffic management systems means a constant spend to change and upgrade. This is not a small cost to the users or the cities.
As a result of new technologies, many social questions regarding employment have arisen, but they also raise some concerns. For example, will those who have worked in the traditional transportation industry lose their jobs? If a machine can perform a task more efficiently and accurately than humans, what happens to those who used to do it?
A machine can sometimes perform a task better than a human, for example, as Nvidia has developed an algorithm that can read traffic signs more efficiently and accurately than a human can. Machines may eventually replace workers who maintain traffic signals. In the age of AI human beings need to upskill and learn new technologies to remain employable.