Road safety witnesses the need for AI

We all are aware of the popular terms that are being used nowadays. Like, Artificial Intelligence, which is emulating human intellect in machines that have been conditioned to think and behave like people. On the other hand, we may be acquainted with the term Humanoid which means a machine with human-like traits such as learning and problem-solving.

Artificial Intelligence, being one-of-its-kind avant-garde technology has gained prominence over the last few years in the arena of road safety too. It has been found by researchers and industry experts that the presence of Artificial Intelligence in traffic management could effectively solve the various problems that we encounter in terms of road safety. With this, let’s take a quick glance over the scenario of comprehensive traffic management and road safety using Artificial Intelligence.

When it’s about traffic management and road safety, it has been witnessed that accidents still opine countless lives every year, despite several optimistic initiatives to prevent them. Every year, around 1.35 mn people die in car accidents, and another 20 – 50 mn get non-fatal injuries as per the WHO, 2020 stats.

Approximately 80% of accidents are the result of human mistakes, such as distraction, excessive driving, or an improper lookout. There are currently numerous safety measures in place to reduce the seriousness and frequency of accidents, including airbags, seatbelts, and speed restrictions.

In India, traffic accidents claim the lives of 400 individuals on average each day. A startling 434,244 lives were lost in traffic accidents between 2018 and 2020, or nearly 150,000 each year. The numbers are down, but owing to the increased use of Artificial Intelligence (AI) towards greater road safety, they are prone to improve in the nearing times.

AI-assisted traffic management has the potential to transform urban transportation by removing barriers that clog up traffic in metropolises. As a consequence, we can witness a decrease in pollutants as well as road congestion and commute times by mitigating the duration we spend in traffic.

In this digital realm, deep learning, IoT, and Artificial Intelligence in traffic management can save lives. AI will advance along with unsupervised learning, pattern recognition, and processing power. Three AI-based features found in Tesla’s Model 3—obstacle, traffic sign, and cut-in recognition—explains the technology’s potential to improve traffic safety. Particularly encouraging is the use of convolutional neural systems for image analysis and recognition.

Contemporary Smart & Integrative Traffic Management Systems would be a terrific idea for enhancing traffic flow on metropolitan highways, lessening the mental anguish of commuters, and even preventing fatalities in traffic accidents. In order to recognize and classify vehicles that are breaching traffic laws and to provide real-time notifications to the Central Command Center, an Intelligent Traffic Management System (ITMS) integrates Artificial Intelligence with surveillance cameras at traffic junctions.

In accordance with the law, penalties can be levied instantly on violators and delivered to them digitally. In addition, to enhance traffic conditions, the Intelligent Traffic Management System supports other smart city objectives like environmental stewardship. For example,

  • Automated Number Plate Reading
  • Red Light Violation Detection
  • Speed Violation
  • Triple Riding & No Helmet Detection
  • Free Left Turn ObstructionWrong-Way Driving
  • Hot Listed Vehicle Detection
  • Traffic Flow & Congestion detection, etc are all techniques that can be used to enhance traffic behavior & avoid overload in a city.

To give a comprehensive solution to the current traffic problem, an Intelligent Traffic Management system can function with the already installed CCTV and traffic control system. Additionally, with a few little tweaks and improvements, different AI-based traffic data can be implemented over current traffic enforcement and monitoring equipment as well, producing a highly optimized solution at a reasonable economic option to the general public.

Artificial Intelligence in traffic management & road safety done right!


The administration of road traffic has undergone a significant shift as a result of the quick development of Artificial Intelligence in traffic management. The movement of people, things, cars, and cargo at separate intervals on the transit network may now be predicted and controlled by AI with high precision.

AI is making it feasible to prevent accidents by optimizing flows at crossings and enhancing safety during times when roads are closed because of construction or other activities, in addition to providing citizens with superior service than before.

Additionally, efficient mass transit, like ride-sharing services, has been made possible by AI’s capacity to process and analyze enormous volumes of data. So what does the scenario of road safety using Artificial Intelligence look like? Let’s check that!

Safety & Security

Since customers and passengers really have to feel secure that they or their things are in good hands, safety is possibly the biggest concern in the transportation industry. It has been claimed over the years that road safety using Artificial Intelligence is a sure-shot solution to foster safety and security during traveling and transportation.

Over the years, technology has dramatically facilitated raising survivability, and today, with the emergence of AI technologies that are being progressively used by organizations and enterprises working in the transportation sector, safety levels may soon reach even greater peaks.

Reliability & Dependability

The dependability of services is reportedly another important factor for many organizations or enterprises engaged in the travel or transportation industry, as passengers are significantly less inclined towards traveling with drivers or in automobiles that appear to be or are considered to be troublesome. One of the main factors influencing the industry’s embrace of Artificial Intelligence in traffic management is its application of public transportation to improve service efficiency.

With the help of Artificial Intelligence, it is desired that transport and travel carriers will be able to arrange both public and private transport services more effectively, as well as process and predict data and outcomes in much greater volume than humans are currently capable of.

Efficiency & Effectiveness

Being energy-efficient is becoming a more crucial component of travel and transportation as passengers’ commutes and trips become increasingly technologically integrated. While this undeniably has perks, it also implies that technological advances will be required to handle their power sources far more proficiently.

Artificial Intelligence in traffic management will surely increase the effectiveness of the systems they interact with, but in order for all of the systems to come into use to fully explore the potential of more advanced technologies, power will have to be spent much more wisely.


In light of the article’s assertion that a substantial part of the globe is growing more environmentally conscious, it is necessary to gradually reduce the number of toxic pollutants in the travel and transportation sectors in order to ensure their long-term viability. AI can also be helpful in this sense.

Artificial Intelligence in traffic management could have a significant impact in creating and implementing innovative new approaches to cope with environmental damage. Artificial Intelligence could also better facilitate scientists and engineers to keep coming up with significantly more environmentally friendly approaches to influence and operate vehicles and machinery for transportation and travel.

All of these elements help to optimize the transportation system as a whole. This also benefits every road user, including individuals who could only engage in traffic to a limited degree before the invention of digital instruments.

Traffic-related concerns taken care by Artificial Intelligence

Autonomous Vehicles:

Several automakers around the world, notably Waymo, Tesla, and BMW, are developing and testing autonomous vehicles, also known as self-driving cars. The technology will be capable of transporting passengers between locations without the use of a driver by combining sensors, cameras, and AI.

Offered Solution:
Compared to conventional, manned vehicles, automated vehicles can provide a variety of benefits. For instance, they can assist in lowering engine emissions and energy consumption caused by stalling automobiles. They can digitize parking procedures, giving drivers so much time to work efficiently. The ability of automated systems to differentiate between various road users is advancing, which can make things safer.

Actual Instance in Reality:
The widespread use of AVs in private vehicles is demonstrated by models like the Tesla Autopilot, Mercedes-Benz Distronic Plus, General Motors Super Cruise, and Nissan ProPilot Assist. 2017 saw the delivery of 50,000 beers by the initial self-driving truck, owned by Uber and operated by Otto.

Given the significance of shared mobility for public transit, AI-enabled autonomous cars, like Olli, can also be employed in shared mobility to improve public transportation. It is an autonomous electrical shuttle for passengers that has wonderful characteristics like Text to Speech, Speech to Text, and Natural Language Classifier.

The Middle East is prepared for autonomous shared transportation. Dubai wants autonomous vehicle trips to account for 25% of all car trips, while Abu Dhabi has established a test project.

Traffic Management Solutions

Real-time data from different modes of transportation, such as vehicles, trams, and trains, are analyzed with the aid of Artificial Intelligence in Traffic Management. The AI examines this data for trends that might point to security vulnerabilities. After gathering this data, recommendations are made for how to lessen these hazards and cut down on the frequency of incidents. Phoenix is putting in place a new traffic control system that employs AI to synchronize lights. Through the use of this technique, automobile delays in Phoenix were decreased by 40%.

Intelligent Traffic Management System is yet another application of Artificial Intelligence in the transport industry. In order to optimize and expedite traffic management and make our roads intelligent, Artificial Intelligence might be deployed in traffic control and decision-making processes.

This is possible because of its analytics, monitoring, and optimization capacities. Traffic management systems can greatly benefit from AI’s predictive capabilities since they can identify the physical and environmental factors that may cause or result in an increased flow of traffic and bottlenecks.

Actual Instance in Reality:
Traffic control systems all over the world are already employing Artificial Intelligence. Utilizing traffic detectors, Rapid Flow Technologies created a Surtrac system with AI capabilities back in 2012 that could forecast traffic conditions and cut travel times by over 25%.

The Road and Transport Authority (RTA), Dubai, recently launched a proposal for a 590 mn Dirham AI-enabled smart traffic system to guarantee a safe movement of traffic and improved management throughout the city. For instance, to finally defeat the dreadful traffic congestion, Siemens Mobility is evaluating a prototype traffic signal surveillance system across India.

Law Enforcement

Artificial Intelligence in traffic management arena is currently being utilized in law enforcement to track down and apprehend individuals who consume alcohol or are texting while driving. Because of the speed at which cars and people can enter and exit the field of view, this can frequently be difficult for human officials. With Artificial Intelligence, it won’t be a concern anymore.

Using advanced analytics and data processing tools, AI could assist in identifying whether a driver is intoxicated or texting while operating a vehicle and alerting nearby police to stop them.

Actual Instance in Reality:
The latest Motorola Solution radio, which equips law enforcement cars with an AI voice assistant, serves as an illustration of this in practice. Currently, when a police officer mentions a license plate, the intelligence will quickly check up on that data and respond.

Passenger Transportation

Although some argue that it may significantly lower congestion and boost fuel usage. Artificial Intelligence in traffic management can be unfolded and used in a variety of ways. When reacting to an urgent situation, for instance, emergency vehicle clearance enables vehicles like ambulances and fire engines to go around red lights or even other obstructions.

Buses are given precedence at junctions, all thanks to transit signal priority, which reduces congestion and shortens commute times for passengers. Additionally, pedestrian safety systems use embedded sensors in the pavement to monitor pedestrian crossings so that the crossing signal can be changed rapidly.

To assure improved freight administration, machine learning, and AI work together to govern sluggish freight trains, regulate faster passenger trains, decrease the number of ambiguous delays, optimize maintenance schedules, redirect trains, improve productivity, etc.

Actual Instance in Reality:
The Middle East’s transportation and logistics industry offers tremendous potential for incorporating AI and machine learning to improve future mobility. A Memorandum of Understanding (MoU) was officially signed between both the Ministry of Transport in Saudi Arabia and Huawei, a major Chinese technology company.

Huawei will assist in the growth of the nation’s logistical and intelligent transportation sectors by utilizing ground-breaking technology like Artificial Intelligence, Big Data, Cloud Computing, and other innovations.

In order to increase the effectiveness of rail transportation, GE Transportation has made major contributions to the advancement of smart locomotives. The GE Transportation intelligent freight locomotives use algorithmic machine learning to improve the real-time decision-making of the locomotives by collecting a huge amount of information through computer vision technologies.

Enhanced parking systems

Since careless parking can block roads and compromise traffic safety, parking management is a crucial element of the public transportation system. Consider yourself entering the city by car to attend a meeting. You allow additional time to find a parking spot because you are aware that there is a significant amount of work. As you get closer to your location, you become more and more aware of how difficult it will be to locate parking.

Predictive analytics and event forecasting are two solutions brought in for enhanced parking solutions. It helps forecast everything, including parking availability, truck driver rest intervals, traffic congestion, and obstructed roadways.

Actual Instance in Reality:
Parking management solutions, such as precise waiting time estimation, unauthorized parking detection, automatic number plate scanning, simpler time monitoring, and charging, improved parking security, and many more, can be successfully delivered by Artificial Intelligence.

In essence, the monitor is informed when a parking place becomes available by the sensors positioned in the parking space. The time and billing can then be determined automatically utilizing computer vision-capable cameras and computerized number plate readers.

These options significantly cut down on commute time and lessen the likelihood of traffic jams in large public areas. A well-known illustration of such an AI-driven parking management system is Zensors.

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Ways AI effectively supports traffic management

ways-ai effectively-supports-traffic-management-trootech-business-solutions

Currently, AI primarily serves as a “co-pilot” to assist drivers by enhancing their safety, comfort, and/or convenience. Although a few applications increase driver comfort and safety, others assist with predictive maintenance or fuel efficiency, which increases convenience and drives down prices. AI is developing quickly and appears to become savvier day after day.

Improved Traffic Stream:

It’s more beneficial to the environment when there are no traffic snarls. Additionally, this necessitates continued software advancement rather than the utilization of hardware, adding another important environmental factor.

Many corporate procedures, including deliveries, can be optimized thanks to it, which is quite advantageous for the economy. By carefully controlling the flow of traffic, human error, which is by far the common reason for fatalities, may be virtually minimized.

Accident rates may be significantly lowered if the human aspect were eliminated. The phrase “Truck Platooning” refers to the idea of connecting many trucks electronically so they can drive in a convoy on the highway. This creates appealing potential in the transportation industry. Only the leading vehicle in this situation has a human driver. All the ensuing trucks are taken over by AI.

Fewer Accidents Means Greater Safety:

By lowering human error, accelerating the procedure of accident detection and reaction, and enhancing safety, Artificial Intelligence in traffic management could enhance road traffic. By enhancing the flow of traffic, AI has the ability to assist in boosting efficiency at peak times.

By enhancing traffic flows at intersections and safety during times when roads are closed due to construction or other activities, AI is also making it feasible to prevent accidents. In order to analyze real-time data from several modes of transportation, such as vehicles, buses, and trains, AI is utilized in road traffic management.

Seamless Driving Experience:

AI-based technologies can enhance the whole driving dynamics in addition to enhancing safety. To best direct, the driver to the target destination with the least amount of fuss, navigation systems, for instance, include data such as current weather and traffic conditions. Similar information can be utilized as a part of intelligent speed assistance, which can help better fuel economy by advising when to accelerate and apply the brake at the best times.

Some sophisticated in-vehicle amenities are evidently just little upgrades but have a major impact on the convenience of the driver. When the appropriate driver gets behind the wheel, AI technology seamlessly modifies personalized parameters for seats and mirrors, for instance, in addition to saving such settings.

AI in traffic management: Real-life use-cases

In order to deal with the problem of traffic safety and discover its core reason, it is now more important than ever for academics, administration, and the technological and transportation industries to collaborate and make alliances. In addition, utilizing the potential of cutting-edge technologies like ADAS, IoT, etc. can lead to substantial operational improvements, local breakthroughs for the international fleet market, and roadside lifesaving.


A variety of in-cabin gadgets and a cutting-edge cloud gateway that offers fleet managers useful insight, analysis, and reports enable Intel Onboard Fleet Services. A highly sophisticated driver assistance system (ADAS) from Mobileye, a leader in AI-based collision prevention systems, is part of the Solution. The Intel Onboard Fleet Services cloud portal offering comprehensive service can help increase general effectiveness, facilitate preventive maintenance, and lower operating costs.

Additionally, by using the Solution, public sector transport firms might realize large financial gains through lower accident compensation payouts. Comprehensive telematics, including vehicle health and fuel analytics, as well as a special driver grading and evaluation module, are also included in Intel’s solutions.

This can greatly assist fleets in lowering the possibility of mishaps and delays while promoting safe driving habits through focused incentive and reward schemes. Driver training, which triggers 15 distinct inputs to deliver customized coaching recommendations for commercial vehicles, which might lose up to 25 workdays for each accident implicated, is the centerpiece of the solution.

ParkMobile & Google:

ParkMobile, a North American supplier of intelligent parking and mobility solutions, has teamed up with Google to allow its customers to pay for parking directly from Google Maps. Users will have the ability to find their itinerary and easily pay for parking at areas that ParkMobile has recognized, thanks to the addition of the new Google Maps feature. Users of Google Maps will benefit from the new partnership between ParkMobile and Google Maps since they will be able to effortlessly begin a parking session using ParkMobile.

We are aware that users frequently switch between parking and navigation applications. Users can now instantly pay for their parking in more than 240 cities throughout the US thanks to ParkMobile’s connection with Google Pay. This recent update allows individuals to navigate to their desired location and then make parking payments in one seamless user experience. Users can also prolong their parking period through the Google Pay app after it has begun, saving them the trouble of returning to the meter.

AI-driven controlled traffic management is on the cards


In 2021, the market for AI in transport was estimated to be worth USD 2.3 bn. As per one of the reports, it is predicted that the worldwide Artificial Intelligence (AI) in the transportation market will reach a value of over USD 14.79 bn by 2030 and expand at a compounded annual growth rate of 22.97% from 2022 to 2030. The increasing use of AI technology in transportation systems is one of the key factors driving this market’s expansion.

Artificial Intelligence is crucial to the operation of driverless cars. The transportation sector relies on AI technology for learning and reasoning while driving. Artificial Intelligence in traffic management has become more widely used in automated vehicles all across the world. By utilizing AI, the transportation industry is able to improve the safety of passengers, decrease fatalities and traffic jams, reduce carbon dioxide emissions, and lower overall costs.

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