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In recent years intelligent technology has become a part of our daily life. And as technology continues to advance throughout society, new applications of AI are becoming commonplace ”including in transport. This has opened up a new field for businesses and startups, as they continue to develop intelligent solutions for making mass transit more comfortable, accessible, and safe.
Using intelligent systems for transportation has the potential to become one of the most effective ways to improve the quality of life for people around the world. There are already several examples of these systems in use in different industries.
Traffic Management
All over the world, clogged roads in cities are a major challenge to urban mobility. City governments around the world have expanded roads, built bridges, and constructed alternative means of mobility such as rail travel, but the traffic problem remains. However, AI innovations in traffic management show real hopes of transforming the situation.
Intelligent traffic management can be used to enforce traffic laws and instill road discipline. For instance, China’s City Brain project, pioneered by Alibaba is using AI solutions such as big data analysis, predictive analytics, and visual search engine, to monitor road networks in real-time and mitigate congestion.
An efficient transformation system is critical to building a city, and AI-based traffic management solutions are powering next-generation smart cities.
Heavy Goods Transportation
Truck platooning, for instance, which networks heavy goods vehicles (HGV) could be highly useful for vehicle transport companies or for transporting other heavy goods.
In a truck platoon, the lead vehicle is driven by a human driver but the human drivers in the other trucks drive passively, only taking the steering in extremely complex or very dangerous situations.
Because all the trucks in the platoon are connected via a network, they move in formation and simultaneously activate the actions taken by the human driver in the lead vehicle. So, say the lead driver halts, all the other vehicles coming behind do the same too.
Ride-sharing
In the ride-sharing economy, platforms such as Uber and Lyft use AI to improve user experiences by matching riders and drivers, enhancing user communication and messaging, and optimising decision-making.
Uber, for instance, has its own internal ML-as-a-service platform called Michelangelo, which can forecast supply and demand, flag trip irregularities including crashes, and estimate arrival times. Not to mention that newer companies such as Waymo are challenging the more established platforms to scale driverless ride-hailing services.
Route Planning
Enterprises and individuals alike can benefit immensely from AI-enabled route planning via predictive analytics. Ride-sharing platforms already do this: using AI systems to analyse various real-world factors in order to optimise route planning.
For enterprises, particularly logistics and shipping companies, AI-enabled route planning is a great way to build a more efficient supply network by forecasting road conditions and optimising vehicle routes. Predictive analytics in route planning involves the machine intelligent evaluation of a variety of road usage factors such as congestion level, road regulations, traffic patterns, customer preferences, etc.
So, cargo logistics businesses such as vehicle transport services or other general logistics outfits can leverage this technology to lower the cost of deliveries, hasten delivery times, and manage assets and operations better.
Alternative Sources of Mobility
The discussion of AI innovation in transportation is often limited to road transport, whereas players in other transport sectors are leveraging AI solutions to drive innovation in those sectors, such as aviation, shipping, and railway transport. Innovative projects in aviation include intelligent air traffic management, trajectory prediction, passenger management, and so on.
In rail transport, there is intelligent train automation, an example of which is the driver assistance systems fitted into trains by the European rail traffic management
system (ERTMS). Navigation is a fertile ground for AI solutions in shipping and maritime, in order to boost operational intelligence.
Also, advanced business intelligence features across the board, by which transport companies (across sectors) optimise their distribution, marketing, fleet management, etc.
Conclusion
We are currently living in the era of intelligent transportation. Societal transformation brought about by AI will bring about a lot of positive changes in our lives. However, we should not be fooled into thinking that just because a technology is automated it automatically becomes a good thing.
Automation does not automatically mean better. In fact, there will always be tasks that require human decision-making and action. A good transportation system should integrate human-machine interaction at every step of its travel planning and maintenance so that people can feel safe and make efficient use of limited available resources.Â
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Image Credit: Pixabay.
