The invention of the wheel paved the way for the creation of modes of transport which were faster and more efficient than the beasts of burden previously used. From these first simple wagons and carriages to the invention of the first gasoline-powered car in the 19th century to today’s electric and self-driving cars, technology has come a long way in finding new and exciting ways to transport humans and objects from place to place. These have however not been free of various challenges. The problems of road transport are numerous and span issues specific to road users and also to the maintenance of road infrastructure. Accidents are far more common by road than by any other transport mode, probably as a result of increased congestion of the roads. Road use is not limited to cars alone and also includes cyclists, pedestrians and other methods of movement. For this reason several variables must be considered in improving road travel and maintaining the safety of the roads.
Over 64 million kilometers of road are said to exist globally with over 1 billion road vehicles in use. In countries where much focus is placed on the environmental impact of transportation, campaigns are on the rise to construct roads which are easier for walking and also to encourage cycling or public transportation for more sustainable travel. Other major challenges of road travel include accessibility to remote areas as well as construction and maintenance of road infrastructure. To solve these problems, technology is being applied to road transport in ways that seek to improve the existing conditions as well as find new ways to conduct road travel. AI has been applied to this and represents a potential solution to humanity’s growing road transport needs.
AI and Road Travel
AI provides vast opportunities for improving road transport particularly in the areas of sustainability and automation. Studies show that the global market for AI will reach $3.5bn by 2023 and $10.3bn in 2030. Already, exciting new ways have been explored to improve the travel experience, however concerns remain regarding the safety of machines making decisions in place of humans. Regardless of this, artificial intelligence and machine learning techniques are being designed and improved upon for inclusion in the technological realm of road travel. Some of the methods thus considered and implemented include:
Self-Driving Cars: The concept of self-driving vehicles has already taken the world by storm in recent vehicles. These have the ability to use sensors which feed information into analytic systems that are able to process and make decisions similar to those of a human driver.
Logistics Optimization: AI can also be applied in cargo movement by logistics companies to effectively calculate the most efficient routes and methods of delivery. This will ensure that costs are saved and that the goods are well maintained from the point of manufacturing to delivery. Planning teams can employ AI in deciding delivery schedules and proper organization of drivers and trucks.
Advanced Driving Assistance Systems (ADAS): Advanced Driving Assistance Systems (ADAS) are already being used in navigation services such as GPS and radar. In combination with other advanced software, this can be used in aiding or even autonomously performing certain tasks such as parking. Analysis of routes and congested areas can provide the motorist with the available ways to avoid traffic jams.
Environment Friendliness: Fuel efficient vehicles are becoming increasingly in demand due to the effects of greenhouse gases on the environment. To keep up with this demands, new models of efficient engines as well as other less dangerous forms of fuel are being designed and developed with the aid of AI systems
Accident Prevention: AI can be used to adapt infrastructure to improve mobility for vehicles in coordination with cyclists, pedestrians and other road users. This will help to reduce accidents as well as encourage other means of transport.
Assessing Road Safety: The UN General Assembly’s resolution on improving global Road Safety seeks to halve the number of deaths, currently about 1.3 million people annually, from road traffic accidents by 2030. In line with this, Machine Learning can be used to rate road safety and assess road infrastructure to improve safety. Forensic post crash analysis can also point out weak spots in existing infrastructure.
These advances represent much progress that can be made in road travel if technology is judiciously applied to complement human effort and perhaps even replace it where possible. The possible roles of AI in transport must be well studied to achieve success and improve the state of transportation.