What are the application of artificial intelligence in transportation

Applications of Artificial Intelligence in Transport

1) AI in road transport


Road transport is one area where AI has been successfully deployed, enabling incredible levels of collaboration between different road users. Technology companies, automakers, and research groups are investigating Artificial intelligence technologies to develop and design automated vehicles for commercial and personal transportation. Like- cars are based on sensors (e.g. GPS, cameras, radar) combined with actuators, controls and software. Similar technologies can only perform certain driving tasks (e.g. parking). Others will soon replace human drivers entirely. In general, testing automated vehicles in urban areas is more complex because there are various factors such as complex road systems and infrastructure that must predict unpredictable signals of vehicle movement. AI technology can also be used in road traffic management to analyse traffic patterns, traffic volume, and other factors. This can help reduce traffic congestion by providing data to drivers on the shortest routes. AI technology assists traffic flow with traffic lights and signals that rotate in real time to meet on-site traffic flow demands.


AI will become mainstream in a few years by eliminating the possibility of errors on the road and making travel an enjoyable experience.


2) Artificial intelligence in aviation


AI is no stranger to the aviation industry. They have been using it across a variety of operations and value chains for decades. But now we are entering a new era where AI capabilities are at their peak and will have a major impact on the aviation industry. AI is in its infancy in air traffic operations. Advances in automation and computing power, and the use of technologies related to machine learning and data analytics models are being used to improve the growing air traffic.


What we understand as advanced business intelligence can significantly change the way airlines do business in distribution, marketing, sales, distribution, pricing and fleet management.



Another area where AI can bring a change in speed or process is ground handling. Examples of potential use cases include aircraft movement operations (pushback and towing) safety checks, aircraft turn operations, loading, refuelling, catering, unloading, anti-icing, de-icing and on-ramp ground transportation of passengers. Baggage, cargo and mail.


3) Artificial intelligence in rail transport


Railroads were one of the most innovative and prominent aspects of the Industrial Revolution. With the rapid growth of road and air transport, trains have lost their heyday of innovation. The extensive data generated by digital technologies can be a useful tool for railway companies to modify organisational systems, improve performance and create new added value. To leverage the full benefits of digitalization, railways can rely on AI. AI can improve operations, manufacturing and maintenance for train operators and infrastructure managers.


Therefore, it is perceived as a means to improve management, reduce costs, and increase competitiveness. Intelligent Train Automation is one of the most representative examples of utilising AI in railway technology and contributing to Automation of Train Operation (ATO).


ATO transfers responsibility for operational monitoring from the driver to the train control system with varying degrees of autonomy. Today, AI can use the power of data provided by sensors deployed on trains or infrastructure components to collect timely information and recommend actions for safety and maintenance.


French operator SNCF said it expects 80% of accidents on catenary lines that power trains to demonstrate some AI application. Research reports show that predictive maintenance has reduced accidents involving train switches by 30 percent, and the technology is being applied to many rail systems and subsystems today. One of the ongoing AI projects is developing the ability for trains to share ‘health checks’ with fleet managers. Administrators can then monitor maintenance remotely using voice recognition software.


4) Artificial intelligence in navigation, shipping, and port fields


Over the past few years, maritime and inland waterway transport has made significant advances. As transportation becomes more common, the risks to maritime defence increase and advancements in maritime surveillance are required. Further growth in container traffic will require adaptation of port terminals and improved connectivity with the hinterland. As vessel sizes continue to increase, shipping pressures on ports and cities increase.


Digital technologies such as the Internet of Things, big data, and automation are game-changers for this industry. Based on that data, AI helps improve safety, energy efficiency, and optimise logistics by analysing information and deriving insights that drive decision-making. The variety of AI applications used or tested demonstrates that the field is focused on introducing such enabling technologies.


Identifying inconsistencies in maritime procedures improves maritime safety. Automatic Identification System (AIS) helps transmit data such as vessel identification number, position, course, speed and destination. Recorded vessel movements and advanced image recognition allow vessels to be detected even when the AIS transmitter is turned off. Insights gained from analysing this data are used to perform maintenance and technical functions to help vessels increase their energy efficiency and meet emission control standards.


Read Also : Artificial intelligence in public transport

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