Of all the industries that artificial intelligence (AI) is currently subverting, the transportation industry seems to be the one that’s getting along the best. That’s because AI is creating significant opportunities for automakers, cargo, and passenger transportation, and even transportation planning.
The introduction of self-driving technology, predictive maintenance systems, and route optimization modules are but a few of the AI-based novelties expected to change forever the way we move around. In fact, some of those technologies are already in the works and, in some cases, they are being used by transportation companies around the world with varied results.
Naturally, the implementation of AI in the transportation industry won’t come without its particular set of challenges. However, there’s a reason why transportation companies hire software developers to help them work AI into their new projects. There’s optimism that those challenges won’t be enough of a hurdle for the industry to keep moving forward with AI.
Let’s take a look at some of the current uses of AI in the transportation industry, some of the potential uses for the future, and some of the challenges that wait ahead to better understand this coming revolution.
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Current uses of AI in the transportation industry
Since AI is already creating new opportunities and new ways of doing things across various industries, it’s not surprising to learn that the transportation industry already has some pretty great examples of how AI works for it. There are 2 main areas where we can find significant advantages provided by artificial intelligence:
Public passenger transportation:
Self-driving vehicles are nothing new today, to be honest. What’s new is how these autonomous vehicles are being used, especially to transport passengers. For instance, there have been trials with autonomous buses in Finland, Singapore, and China, each of which differs in their particular geographical conditions.
There’s also a pilot for more personalized transportation in Olli, a self-driving electric shuttle powered by IBM’s Watson Internet of Things for Automotive. Olli can pick up passengers on demand from different locations to take them to predefined destinations, while also providing suggestions on places of interest. This makes Olli perfect for tourists visiting a new city because it uses its AI systems to provide something more than just transportation.
Cargo transportation:
Back in 2016, a startup called Otto made a delivery 50,000 cans of beer using an autonomous truck. The company was later acquired by Uber and continued testing with autonomous trucks with one goal in mind – to use driverless trucks to increase productivity while lowering carbon emissions and reducing accidents on the roads during deliveries.
A Chinese startup called TuSimple is working in a similar direction but with the implementation of deep learning algorithms in their AI systems. They use that to simulate the experience of millions of miles of road driving, which would let the onboard computer make inferences and take better decisions while on the road.
Finally, self-driving cargo transportation doesn’t end in autonomous trucks. As proven by GE Transportation, locomotives can also use artificial intelligence to improve the efficiency of rail transport deliveries. Thus, GE’s smart freights include sensors and cameras to feed a machine that makes decisions in real time regarding speed and detection of issues on or around the tracks.
Potential uses for AI in the transportation industry
Though self-driving presents the biggest opportunity for the transportation industry today, there are other potential uses of AI that are being researched or contemplated right now. There are several areas that could benefit from the introduction of artificial intelligence, including:
Increase in public safety:
Though it’s hard to believe, AI in transportation can lead to an improvement in security in public spaces. That’s because public transport could track crime data in real time, aiding the police in their patrolling and increasing their efficiency in crime prevention by identifying and monitoring crime-ridden areas.
Enhancement in traffic flow:
One of the biggest problems in most modern cities is traffic flow and the congestion it suffers. AI could come to the rescue here as well since it could streamline traffic patterns to contribute to a reduction in potential congestion.
This could be done through smarter traffic light algorithms and real-time traffic tracking that could better control and adjust traffic patterns on the fly. Additionally, AI could also be used in public transport to optimize their routes and schedules depending on the specifics of each city and their particular areas.
Better pedestrian and cyclist safety:
AI can also indirectly impact the transportation industry by analyzing pedestrians and cyclists in any given city or town. Through strategically placed sensors, the flow of both of them could be analyzed to provide better circulation channels and to detect opportunities for growth in public transportation to better serve both of these groups.
More informed decision-making:
This is a more broad area that implies a lot of possibilities. AI can be used to evaluate terrains and decide places for new roads that could positively impact cargo transportation, analyze traffic in real-time to divert traffic in case of an accident, suggest which roads need maintenance, predict volumes for road freight transportation, and so much more.
The challenges ahead
As with any new technology, the use of artificial intelligence presents a number of challenges that need to be overcome to become a standard in the transportation industry. Among the most relevant ones, there are 3 that are perceived as the most concerning ones:
Cost of adoption:
AI isn’t precisely a cheap technology to implement, especially in complex systems like the ones used in transportation. In fact, a recent report found that more than half (53%) of global business and IT leaders think of the high costs of AI as a major deterrent to adoption. Considering the cost to hire development teams, purchasing, and engineer equipment, and training employees, this is a major hurdle that the industry will have to overcome.
System reliability:
Another major concern is how reliable the AI systems can actually be. There have been various incidents involving self-driving vehicles that put a question mark on how trustworthy the technology is. It seems that the AI systems still haven’t developed an efficiency level that can make general adoption possible.
Job flow:
Its introduction would mean a threat to existing jobs. The increasing presence of unmanned vehicles would leave millions of trucks, taxis, and other drivers out of work. Some experts argue that these jobs wouldn’t be lost since they can be transformed or evolved into other sectors. However, the perception of job loss surrounding AI in the transportation industry remains the same.
The context for artificial intelligence in the transportation industry appears exciting, promising, and full of potential. Naturally, since it’s such a new technology, there are a lot of worries and questions surrounding its implementation that only future developments, testing, and practical uses could address.
Only time will tell how much of that potential will be fulfilled. Seeing how many companies are betting on it, it seems that we won’t need to wait much time to see more and more AI development making it into the transportation industry.
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