The rise of artificial intelligence improves the performance of information systems and automates operational decision-making.
As a corollary to the digitization of transport, artificial intelligence (AI) is spreading in the sector’s information systems. Taking advantage of a government envelope of 3.7 billion euros to accelerate the development of AI “made in France” over the period 2018-2027, a host of start-ups and transport software publishers are exploiting this technology now.
The integration of algorithms is becoming widespread in TMS, in freight exchanges or traceability platforms, in fleet management software and even in connected vehicles. Algorithms become more efficient and self-learning thanks to Machine Learning functions that allow software to take into account past data and events to better analyze information from the present or even predict the future.
Algorithms that simulate
In carrier information systems, AI offers three main benefits. It quickly processes millions of pieces of information, it automates certain processes and makes it possible to simulate operational scenarios. In the opinion of all, AI is not capable and does not aim to replace the human being to plan the TRM. Rather, it is a decision-making tool by letting IT process an increasing flow of data that managers do not have the time or the means to analyze.
It makes it possible to simulate transport plans or optimal rounds, to compare costs, performance and the ecological footprint between thermal or alternative energy fleets, to automatically build loading plans. As the AI progresses, it should go beyond the simulation stage and become capable of making decisions or arbitrating different scenarios.
AI understands data better
On stock exchanges or digital freight platforms, AI is used to analyze flows and automatically propose freight or transport availability offers to users. The self-learning algorithms process all the data and are able to take into account fuel cost constraints, wage costs or empty returns to determine market prices.
In the route planning modules, the AI processes the information reported in real time by the vehicles in order to recalculate or adjust the routes on the fly to meet delivery deadlines or limit mileage.
AI is also used in today’s connected trucks, tomorrow autonomous. For example, it processes technical data on the state of health of remotely diagnosed vehicles in order to automatically generate wear reports or preventive maintenance alerts and recommendations. It is found in driving assistance systems of the on-board coach type or in solutions for monitoring the vigilance of drivers via on-board cameras.
These are, for example, algorithms that detect inappropriate driving behavior or an external danger requiring the driver to be alerted, or even to take control of the truck’s steering. Autonomous driving software, still in the development and testing phase, all incorporates AI that will one day have to be able to analyze the road as well as humans do before taking control of heavy goods vehicles.
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SITL 2022: How is AI spreading in TRM?
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