Artificial Intelligence (AI) is a technology that fuels machines with human intelligence – machines that have AI capabilities can automate manual tasks and learn on the go just like humans.
Such automation gets repetitive and time-consuming tasks under the AI-powered systems that learn with time and can eventually carry out critical tasks and make decisions on their own.
Such unique potential drove the transportation businesses to start investing into AI technology to improve revenue and stay ahead of their competitors.
Transportation industry has just begun to apply AI in critical tasks however the reliability and safety in transport are still under question. Major challenges in transport like safety, capacity issues, environmental pollution, reliability etc. provide a huge opportunity for AI innovation.
No matter downfalls, AI in transportation is yet to reach 3.5 billion dollars by 2023.
How can A.I. help transportation:
Transportation industry faces problems when a system functionality cannot form per predictable patterns which are affected by external elements like traffic, human errors or accidents.
A.I. uses data to predict decisions appropriately and it has been implemented in a variety of ways where some of examples include
- public safety (e.g. tracking crime data in real time),
- autonomous vehicles (e.g. self-driven vehicles),
- pedestrian safety (e.g. tracking pedestrians/cyclists paths to minimize accidents),
- traffic patterns (e.g. causes of delays, reduction of traffic congestion) and
- corporate decision making (e.g. accurate prediction methods and forecasts).
- etc.
Benefits of AI in transport:
The best fit of AI and transport somehow came naturally as adoption of these technologies can have a massive impact on the entire industry although the application of AI still varies across geographies.
Increased use of A.I. will ensure reduced labour costs while providing higher profits – fully automated fleets will be there to resolve an issue of long driving hours and breaks.
AI can also have a huge impact on safety and traffic accidents. Driving at night is a great issue and smart unmanned vehicles can significantly improve the problem. Auto-pilots or unmanned vehicles that can operate without a human can help the drivers to snooze without causing any traffic accidents.
Traffic management can also be more effective – AI methods enable us to forecast traffic by using traffic data and details about urban ongoing events as well as suggest alternative routes by automation.
Complex infrastructures and various elements within cooperation chains can be improved with the help of AI through e.g. optimal route schedule, minimal waiting time, traffic detection in real time for adjusting the routes etc.
Data analytics in logistics can also help upgrade transportation planning and increase safety in general.
There are many more benefits to list yet AI is still growing and the benefits will too.
Drawbacks of AI in transport:
Although AI can bring a plethora of benefits to any industry, there are also some drawbacks to pay attention to like creating transparency in AI decision making or safety issues within autonomous vehicles.
One of drawbacks include job flow as it became a major issue for truck or taxi drivers and other industry members. Although social experts tried to explain that job skills can be shifted to other sectors, tensions remain.
AI implementation can also represent drawbacks as undeveloped countries will face massive challenges in utilizing such solutions as their infrastructure is not capable of providing maintenance.
Transport companies also face potential AI drawbacks as transportation costs contribute to the company revenue by up to 10%. All existing businesses will need to develop and implement AI technologies to remain competitive within their industry and the high cost of developing, repairing and maintaining the complex machines can be extremely high.
Ethic issues are also under revision – companies bringing AI to transportation will have to take the ethics of AI seriously if they want to win consumers’ trust.
AI technology still needs significant improvement as it’s still far from a human level of intelligence.
AI in Transportation
We can already find the examples of AI being used within transportation. As Artificial Intelligence evolves and becomes more mature, it is certain that the number of roles where AI plays a major role will increase.
Some of the examples where AI is common in transport are:
Autonomous Vehicles
Autonomous vehicles are already a reality within transportation. They represent the first step into a new future of autonomous transportation although it seemed quite a futuristic idea in the past.
A.I. uses its processing, control and optimization capabilities to power these driverless vehicles. With autonomous vehicles, real-time data transmission and processing is crucial and any confusion within these processes can cause fatal outcome.
In Tokyo, autonomous taxis have already begun operating but the driver sits in the car for safety reasons to be able to take control of the taxi in case of emergency situations.
The USA started to implement autonomous trucks as of 2018 as 65% of goods are carried via trucks globally. It has also been predicted that maintenance and administration costs can be reduced by around 45% with autonomous trucking.
Waymo company has introduced self-driving minivans and trucks within a certain number of states for testing on public roads.
These types of projects are still in pilot versions while trying to make autonomous vehicles safe for passengers but as the technology evolves, autonomous vehicles will gain more reliability and become much more widespread.
Source: youtube.com
Passenger Transportation
The safety of passengers, pedestrians and drivers has always been a number one issue for the transportation industry.
The use case of AI was the autopilot system which is used in almost every commercial airplane today and it is a vital part of any air travel nowadays.
New York Times reported that only 7 minutes of an average Boeing flight is controlled by humans (takeoffs and landings) while the rest is being handled by an autopilot.
Public transportation will also be hugely affected by A.I. since there have been driverless buses recently transporting passengers with the help of sensors, cameras and GPS.
A.I. technology can do so much for us – it can reduce the human errors within traffic, monitor the safety regulations compliance, review vehicle maintenance reports and manage transportation in such manner to decrease effects of driving risks in urban areas.
Smartphone Apps
Today, ‘there is an app for everything‘ – it includes AI powered real-time traffic updates through services like Google Maps or Waze.
These applications use location data collected from smartphones to be able to analyze traffic conditions within your local area and inform the users better about the traffic.
These apps may soon face competition in the form of autonomous vehicles as why would you need an app if the car itself already does the same work?
Traffic Management
AI can be used to manage traffic systems due to its processing, control and optimization capabilities. In order to make the roads smarter, AI can be applied to traffic management to streamline the traffic. AI prediction ability to recognize the physical or environmental issues that may lead to heavy traffic or congestions is another great benefit.
Sensors and cameras implemented on the roads collect large amounts of data related to traffic details. Such data is analyzed by big data and AI systems to reveal traffic patterns.
The relevant insights assist commuters with details on traffic predictions, accidents or road blockages and provide suggestions on the shortest routes to their destinations.
The example is Siemens Mobility which has tested a prototype A.I. monitoring system through traffic cameras – it alters the traffic lights based on real-time road congestions hence minimizing road congestion.
This way, AI can reduce redundant traffic, improve road safety and reduce wait times.
Law Enforcement
Law enforcement is another area where A.I. is not being used to help law officers to identify people drinking or texting while driving, for example. Earlier, this represented an issue for human officers since vehicles move at high speed but A.I. has resolved this issue.
Now, A.I. can detect quite accurately if a person drinks or texts while driving and sends an alert to any nearby officers to intercept them.
One of those examples is Motorola Solution which brings AI voice assistants to law enforcement vehicles where police can just state a license plate and the system will look up the information and respond within a few seconds.
Delay Predictions
Another problem within transportation, especially air transportation, is the delays. As per the research paper by the University of California, Berkeley, flight delays can cost up to 39 million dollars in the USA.
In order to overcome flight delays costs as well as passengers’ negative experience, AI comes to the air transport for rescue.
Data analysis and computer vision come to assist and shorten passengers’ waiting time as AI can use its capabilities to predict anything from bad weather to a certain number of technical glitches which may cause flight delays. Computer vision systems can intermittently monitor the aircrafts while AI along with Machine Learning (ML) use and process real time aircraft data, records and weather information.
Computation can reveal some hidden patterns helping the industry to gain valuable insights on possibilities that may cause delays or cancellations.
Drone Taxis
Drones have already been used in delivery service systems but soon they could be used as taxis as well. Unmanned aerial vehicles represent a one-of-the-kind solution in fight against carbon emissions, traffic congestion or expensive infrastructure.
Drones as taxis will allow people to arrive at their destinations much faster while reducing their commuting time to a minimum at the same time. Regarding urban areas, drone taxis can be a real deal for solving the issues like urban planning and urban infrastructure development.
Some recent examples of drone taxis was a display of an autonomous aerial vehicle in China, where 17 passengers had a chance to experience smart air mobility for the first time.
A few years ago, Uber took a step forward to make autonomous ‘flying taxis’ a reality by signing a partnership with NASA to develop the software to operate them.
These use cases indicate that similar future applications are about to be developed.
Finale
Artificial Intelligence (AI) has become more than just a visionary idea – it is a part of our daily lives and we use it each day without even noticing. AI can be found in our mobile apps, in social media feeds or in the way how Grammarly checks our grammar mistakes.
Transportation industry has already used some AI solutions for a while but it won’t be long until the increase of AI within transportation and logistics.
As A.I. is getting more subtle with time, it is a matter of time when we will get to see the exciting future driven by AI!