The digital age has changed almost every single aspect of our lives, for better or worse. Nowhere is this more obvious than in the way we have slowly but surely moved away from manual methods of not only map making but road management, and towards a future where machines will do the majority, if not all of the heavy lifting.
Regardless of whether we prefer traditional, hands-on methods of road management, or even prefer a physical map over a digital one, technology is going to be essential, not optional, in the future of roads all over the world. With an estimated 2 billion vehicles to be on the road globally by 2040, experts claim that the demands of keeping motorists and pedestrians safe, as well as ensuring that cities can be navigated functionally, the use of AI, machine learning, 5G will become even more essential to road safety and management in the years to come.
The Gift of Convenience
At its most fundamental level, technology helps make our lives more efficient and convenient and road management is no exception. While the merits of integrating technology also extend to important factors such as safety, it is everyday details such as accurate traffic reports and estimated times of arrival that will noticeably impact the daily routines of motorists and commuters.
GPS has been used now for decades to help map our roads and create a revolutionary way of finding our way for road users and pedestrians alike. However, this method clearly has its limitations, namely in areas restricted by trees or locations with bad signal, such as tunnels. The limitations of GPS accuracy also carry over to measuring accurate levels of congestion in real time. As we know, the majority of major cities in the world are still characterised by constant congestion and average speeds of about 4k/mph in peak times in cities like Bengaluru in India.
AI is already being used in countless locations to combat these issues and create more efficient cities by predicting and detecting incidents and jams through cameras and machine learning, feeding back and sharing information far more quickly and accurately. With the use of AI and machine learning applications, road systems can not only be read to interpret real time data, but in some cases, are able to predict upcoming traffic ahead of time and warn for abnormal congestion based on a number of factors, such as special events or even weather.
This allows for an easier, more efficient journey for the driver, who has a much more accurate estimated time of arrival. It also allows for human involvement in day-to-day traffic management to be minimal, which frees them up for what some have called “higher level work”, including interpreting AI data, adaptive thinking and creative solutions.
Keeping Costs Down
As human intervention becomes less and less necessary for road and traffic management, one of the clear benefits to this is a decrease in cost. Of course, AI is nowhere near advanced enough yet to function without some sort of human analysis of at least some of the data, but mundane tasks such as watching over traffic cameras will one day be a thing of the past.
However, this also extends far further than costs incurred in road and traffic management itself. As AI improves and is able to better predict congestion and is accurately able to reroute more efficiently, journey times could be cut considerably. This means industries reliant upon transport that are often at the mercy of potential congestion and road incidents, will be able to complete their daily tasks faster and more economically.
AI has also been and continues to be instrumental in road safety. Accidents and incidents are expensive, and the UK alone loses millions of pounds a year on accident-related costs alone. The use of AI to make road safety and accident prevention more reliable can help to reduce these costs further.
Road safety is not only just important in order to keep road management costs down; it is essential in saving lives and preventing injury at a time when there are more cars on the road than ever before. By utilising technology, such as drones and camera-equipped vehicles to aid in road maintenance, engineers are able to find damaged or hazardous areas much faster.
However, AI can also aid in accident prevention by collecting data from cameras to perform real time checks for drowsy and reckless drivers. As well as being able to locate dangerous drivers and damaged areas of the road, historic data can also be analysed to ascertain where the most dangerous segments of a road are located based on previous incidents.
When it comes to road safety, AI is also instrumental in ensuring that the future of self-automated vehicles is a safe and reliable experience for drivers and pedestrians. Through the use of camera technology and machine learning, digital maps are able to become more detailed to aid with the needs of the self-driving car. Camera technology is also far better equipped to recognise and register smaller details such as road signs, which may not be picked up by GPS and are essential information for the self-automated vehicle.
Even while self-driving cars are still considered mostly a futuristic invention, standard vehicles can also benefit from accurate digital mapping in terms of safety. More advanced maps with greater detail for upcoming speed variations, slip roads and other vital information allow the driver to plan ahead and drive more safely.
What are the limitations of technology?
Of course, we are nowhere near an age where computers and robots are able to tackle every single road management issue and task we have. AI in particular is still very much in its infancy with understanding what we need it to understand.
One of the biggest roadblocks when it comes to both AI and machine learning is that it is currently a mostly outcome-driven process and understanding how these applications arrive at their outcome can be a challenge. The number of processes that an AI unit must go through to achieve the accurate outcome we need makes it almost impossible to realistically dissect its reasoning and figure out how it came to its final conclusion.
Using incorrect algorithms or data models can also result in biased or misleading results. This is why a certain level of human intervention is still necessary in order to reduce the likelihood of blindly trusting the data produced by an AI algorithm. A certain level of analysis and statistical assessment is needed in order to achieve the most accurate results.
The most important thing to remember with any kind of road technology and especially with AI is that there is no one-size-fits-all approach; any technology must be used in tandem with analysis and human critical thinking. AI is still a relatively new technology all things considered and as such, is still very narrow minded in what it can achieve. What it can do is create a more efficient, safe and streamlined process when it comes to movement of people, road safety and infrastructure; something that is going to be even more vital as we really move into the future.