Introduction:

One of the burgeoning fields reshaping the transportation business is artificial intelligence (AI), which is receiving attention. As machine learning techniques have been integrated with technologies for searching and analyzing the massive amounts of data (also known as big data and data mining) produced by the advent of the digital world, AI has advanced significantly. Other factors contributing to its successful development include advancements in transportation technology, the internet of things, and communications networks. Though there is disagreement over the timing and precise nature of these improvements, the advancements of AI in transportation are anticipated to be even more astounding. All forms of transportation are becoming cleaner, smarter, safer, and more comfortable thanks to AI.AI can be used to provide transportation services in vehicles, infrastructure, drivers, or passengers.
AI aids in the analysis of travel demand and pedestrian behavior as well as the detection of market trends, risk identification, traffic congestion relief, and the reduction of greenhouse gas and air pollution emissions.

Five Examples of AI in Transportation:

AI is currently applied in the transportation industry in a number of different ways. It is almost certain that the number of positions that AI can take and manage will expand dramatically as the field of transportation AI develops and matures.

1. Autonomous Vehicles: Autonomous vehicles are among the most exciting new transportation ideas to become a reality, and they may well be the first step toward a new era of autonomous mobility. These driver less cars require artificial intelligence because of its processing, control, and optimization capacities. Real-time data transmission and processing is essential for autonomous vehicles, and any delays to these operations could have disastrous consequences in the real world. The capacity of an AI to control data transmission and processing, as well as connectivity optimization to guarantee the optimal connection is always used, will contribute to the safer and wider adoption of autonomous vehicles.
2. Smartphone Apps: Real-time traffic information is provided by AI via apps like Google Maps or Waze. These apps are able to forecast and analyze traffic conditions in your neighborhood to better inform your travel plans by leveraging location data gathered from users’ smart phones. But given that driver less vehicles themselves may eventually provide direct competition, these apps might not be available for very long.
3. Traffic Management Solutions: The traffic management systems in transportation use artificial intelligence technology. In order to improve and streamline traffic management and make our roads smarter, artificial intelligence might be applied to traffic management and decision-making systems. This is possible because of its processing, control, and optimization capabilities. 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 increased traffic flow and congestion.
4. Law Enforcement: Artificial intelligence is now being used in law enforcement roles to aid in the detection and arrest of drivers who are intoxicated or texting. Due to 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, this is no longer a problem. AI might be able to recognize when a driver is texting or drinking behind the wheel and inform any nearby officers to stop them. This is done by applying advanced analytical and data processing capabilities.
5. Passenger Transportation: Self-flying aircraft have been available for some time. People are frequently shocked to learn that practically every commercial airplane currently in service uses autopilot systems, which were one of the first applications of artificial intelligence in the transportation industry. While it might not sound as futuristic today as other uses of AI that are being explored, it is nevertheless a crucial component of any modern aircraft.

AI Applications In Transportation:

1. Self-driving Vehicles: In the current era of AI transportation, businesses can employ computer vision methods like object detection to build smart systems that can decode and comprehend visual input, essentially enabling a car to drive itself. While the idea of creating an AI for a self-driving car may seem complicated, it is actually quite simple: the algorithm is fed vast amounts of pertinent data before being trained to detect specific objects and then take the appropriate actions, such as braking, turning, speeding up, slowing down, and so on.
2. Traffic Detection: AI-based system can be trained to recognize lights—green, amber and red—via computer vision models that are trained in a wide range of scenarios, such as poor light conditions, inclement weather and occlusions. As such, a self-driving car’s cameras first spot a traffic signal, before the image is analyzed, processed—and, if it turns out that the light is red, the car puts the brakes on.
3. Pedestrian Detection: Pedestrian detection is actually a key problem in computer vision and pattern recognition because pedestrians can be highly unpredictable in the context of road traffic. They’re so unpredictable that they pose one of the greatest threats to the success of self-driving cars. The key is not necessarily that a system recognizes specific human features, such as beards and noses, but that it’s able to properly distinguish a human from another object, as well as understand what a pedestrian is planning to do next.
4. Road Condition Monitoring : Road condition monitoring has largely been left in the hands of citizens, whose “task” is to raise awareness of damaged roads to their local councils. Potholes can be recognized using computer vision algorithms, which can also display the extent of the damage to the road so that the appropriate authorities can act to enhance road maintenance. To build automatic crack detection and classification systems, the algorithms first gather and process image data. These will promote preventative maintenance and focused rehabilitation that doesn’t involve humans.
5. Automated License Plate Recognition: Automated license plate recognition involves the use of computer vision camera systems attached to highway overpasses and street poles to capture a license plate number, as well as the location, date and time. Automated license plate recognition can use new camera systems designed specifically for this purpose, or it can use existing CCTV, as well as road-rule enforcement cameras.
6. Driver Monitoring: For better, safer driver monitoring, computer vision has now been introduced to automobile interiors. The system potentially stop hundreds of accidents and fatalities each year by using face detection and head pose estimates to look out for things like tiredness and emotional recognition. This is crucial since many drivers are reluctant to acknowledge when they are drowsy or exhausted and how it will affect their ability to drive. When a driver’s performance is significantly being hampered by weariness, AI-driven technology can inform them and suggest they pull over and rest.

Challenges AI Can Solve in Transportation:

1. Safety: Safety is arguably the most important consideration for those working within the travel or transportation industries. In order for services to be successful in any shape or form, passengers and customers need to know that they or their belongings are in safe hands.Technology has made increasing safety levels much easier over the years and now, with the advent of AI technologies that are becoming increasingly adopted by businesses and enterprises operating within the transportation arena, safety levels could about to reach even higher peaks.
2. Pollution: In addition to helping engineers and scientists develop much more environmentally friendly ways to power and operate vehicles and machinery for travel and transportation, artificial intelligence could play a significant role in developing and implementing new and innovative methods of dealing with pollution. AI is used in transportation in a number of different ways. It is almost certain that the number of positions that AI can take and manage will expand dramatically as the field of transportation AI develops and matures.
3. Efficiency: As modern travel and trips become more and more reliant on technology, energy efficiency is becoming a more critical component of travel and transportation. While there are clearly advantages to this, it also means that emerging technologies will require much more effective power supply management. Artificial intelligence technologies will surely increase the effectiveness of the systems they integrate with, but in order for all of the systems when used to fully realize the potential of newer technologies, power will need to bused much more wisely.

Conclusion:

Artificial intelligence in transportation is leveraging important advanced technology, such as big data in transportation for improved safety and machine learning for greater efficiency, so that towns and cities—as well as smart cities are able to reduce the number of road accidents, improve the flow of traffic, and even bring criminals to justice. As the technology continues to improve, the hope is that more smart cities will appear around the world, boosting worldwide operational efficiency, enhancing sustainability and making our roads, highways and intersections safer and better for all.