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AI in Transportation


Artificial Intelligence (AI) in transportation has always seemed like sci-fi as it’s been part of the entertainment scene for so long. Whether you remember Knight Rider, the DeLorean, a flying car in Grease or happen to be a fan of Herbie, thinking up ways of innovating transportation has clearly been at the forefront of creative minds. However, today, it is not so futuristic and thanks to AI, many of the fantasies from the movies are coming to life. Breakthroughs in technology are being announced virtually every month and, if you listen to Elon Musk (Tesla), it won’t be long until AI has taken over the transportation industry. This article looks at some of the ways in which AI is transforming the sector.

Autonomous/Driverless Vehicles

When we talk about autonomous vehicles, there is more to it than the cars we’ve been promised for a while now. Cars have been trialled but are still in their early stages and don’t look set to become mainstream too near in the future. However, there are plenty of other applications that are making best use of the technology. Small scale autonomous buses have been deployed in some parts of the world. China, Singapore and Finland are the countries where it the AI has been used most. The UK started trialling autonomous buses in March 2019 and there are plans to launch more pilots. These solutions still have human drivers behind the wheel in case of emergency. Although the AI is advancing, it is not able to think for itself in a way that makes it safe to leave it to its own devices. With that in mind, it is likely that autonomous buses will become mainstream technology way before driverless car, purely because of the human resource requirement. In Texas, the same technology is being used for grocery deliveries whereby a human operator is always at hand, but the system is very much AI driven. With several sensors and detectors, the AI systems can provide alerts about dangers like cyclists or pedestrians. The result would be safer roads as accidents are most frequent when human drivers don’t spot these hazards. At this level of automation there are lots of potential applications. Garbage trucks, snow ploughs and other large vehicles could be made more efficient using AI with a human overseeing what is going on. Driverless trains have been tested with success on the London Underground. One of the benefits here is that it frees up critical space through not requiring a drivers’ carriage. Anyone who travels on the underground regularly will know how vital space is. One of the smartest solutions may well be remote controlled cargo ships. It is thought that these have been in the making for a long time but if deployed, the savings on crew costs would be a massive win for the industry. Many driverless vehicles are also powered by electricity making them far more environmentally friendly.

Traffic Management

Complex data algorithms can be used to manage traffic and create redirection routes if required. Traffic sensors are already being used to predict potential accidents based on current conditions and helping to make recommendations on speed limits or routes. In 2012, the Surtrac AI system was installed in 9 traffic signals within the Pittsburgh area. By using data, it was able to reduce travel times by more than 25% on average and reduce wait times by as much as 40%. The solution was so successful that it was added to other traffic signals around the city. In Bengaluru, India, which regularly faces long traffic jams and the average speed on some roads at peak hours is just 4km/h (2.5mph), Siemens Mobility has built a prototype monitoring system that that uses AI through traffic cameras. Traffic cameras automatically detect vehicles and this information is sent back to a central control centre where algorithms estimate the density of traffic on the road. The system then alters the traffic lights based on real-time road congestions. Traffic management requires a lot of data and with so many cameras on the road and enormous computing power, we now can make it successful. Just imagine a world without traffic jams!


Amazon are starting to take to the air for deliveries. They are testing drone technology to allow for faster and safer delivery of goods to customers. It is an expensive investment but one that could really push them way out ahead of the competition. Uber have trialled a drone taxi in Dubai and whilst the future of that technology isn’t known yet, it is amazing to think that it’s available. Digital Number Plates Over time, we can expect cars to start losing physical number plates as they become digitalised. There are several benefits to this system such as notifying authorities instantly if something is wrong with your GPS location. The amount of vehicle theft will naturally reduce as it will be impossible to get away from a system that knows exactly where your car is using AI.

Air Travel

Pilotless planes have been talked about already, but the challenge will be in gaining public trust for such a huge innovation. Whilst there arguably isn’t much a pilot needs to do during travel (without disrespecting what they do), having nobody there might cross a mark just now. However, the way we travel is likely to change with proposals for digital passports based on face scanners at airports and tracking baggage via GPS to ensure it never gets lost.


AI brings a number of benefits to the transportation industry. With the ability to improve efficiency, provide better customer experiences and reduce accidents to name a few benefits, AI will be a driving force over the next decade in the sector. This article only considers some of the main developments with robot police cars, driver assist programs, AI taxi hailing and smart highways all on the industry radar. According to the US transportation research board, emerging applications of AI in transportation planning are in travel behavioural models, city infrastructure design and planning, and demand modelling for public and cargo transport. On-demand services like Uber are also likely to start moving to entirely autonomous services over time as long as they have more successful trials. Ethical constraints may mean it takes a while to get to full adoption in some cases but what originally looked like sci-fi, is now a distinct possibility.