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There is no doubt that artificial intelligence (AI) has become the talk of the town, with the technology seeping into the daily lives of consumers, who have utilized generative AI to have fun, do homework, and for other purposes. Nevertheless, AI has been present in various businesses across various industries, including logistics and, by extension, road freight transportation. The technology has helped companies optimize their operations, providing operating cost savings, including fuel, and thus lowering emissions.
However, one of the main issues with AI is that the abbreviation has become one of the go-to buzzwords in the corporate and consumer worlds, straying away from its actual meaning and the kind of benefits it can provide to businesses, including road freight transportation.
Early developments of AI in road freight transportation
Already in 2019, which seems far away from the current boom of AI that has been happening everywhere, the International Finance Corporation (IFC), part of the World Bank Group, issued a paper describing how the technology was already making transport safer, cleaner, more reliable, and more efficient.
While the report focused on emerging markets, the note began by saying that AI was “already having a profound impact on the way we interact with the world around us.” The article’s authors continued that technology can help humans solve everyday problems, with AI having significant applications in several fields, including transport.
“From scanning traffic patterns to reduce road accidents and optimizing sailing routes to minimize emissions, AI is creating opportunities to make transport safer, more reliable, more efficient and cleaner.”
Speaking about road freight transportation, or modes that are part of a supply chain, the note highlighted that transport, which moves people and goods, depends on consistent performance and the ability to predict arrival and departure times, which applies to road freight transportation as well, with companies having to run supply chains tightly due to limited working hours of drivers.
The report stated that uncertain, unreliable, or congested infrastructure negatively impacts the reliability and predictability of movements within the supply chains. While the note exemplified by using solutions utilized by ride-hailing apps, as well as public transportation providers, self-learning models can still impact how freight is transported on the road, including the efficiency of supply chains.
“AI can help optimize movements in order to maximize efficiency. In particular, the field of e-logistics—in which Internet-related technologies are applied to the supply and demand chain— also incorporates AI in several ways, such as matching shippers with delivery service providers.”
Regulators’ point of view
Similarly to the IFC paper, the European Parliament (EP) issued a briefing about AI in transportation in March 2019, overviewing the then-current and future developments of the technology in road transportation.
One of the key things the briefing mentioned was that the EP was taking steps to adapt regulations to support innovation and, at the same time, ensure respect for fundamental values and rights. As such, the briefing provides another point of view, this time from the regulators’ side.
And another noteworthy statement from the briefing was the fact that the EP explained that AI is not a single technology but rather “a vast set of diverse approaches, methods, and technologies, which to different degrees and in different ways show intelligent behaviour (such as logical reasoning, problem-solving, and learning) in various contexts.”
The briefing pointed out that road transport was where AI has been successfully applied, opening up cooperation between different road users. Nevertheless, one interesting case was platooning, namely the coupling of several heavy goods vehicles (GHV) within minimal distance of each other, enabling them to move in unison. A human driver would lead the motorcade, with testing having been conducted at the time. Still, the EP’s briefing said that further tests are needed to test the technology, especially in more complex traffic situations.
The briefing concluded that while AI “brings great benefits to road transport,” it also poses serious challenges, especially in mixed-use environments, with the EP also mentioning cybersecurity and ethical challenges, especially in use cases where AI is driving a vehicle.
Other use cases
According to an article by DHL in November 2018, logistics and AI fit like a glove, especially since companies depend on physical and increasingly digital networks, with them having to “function harmoniously amid high volumes, low margins, lean asset allocation, and time-sensitive deadlines.” As a result, instead of relying just on humans and their thinking, companies, for example, can utilize various language models to combine the two and achieve unprecedented efficiency gains, enabling companies to process and utilize data that otherwise would have been lost due to the limitations of time and sometimes, the human ability to process vast amounts of data.
Still, the company stated that one of the most revolutionary ways that AI will change logistics is predictive analytics, which will allow shippers and carriers to become proactive rather than reactive. Naturally, being proactive means that companies will be more in control of their operations and assets, increasing revenue opportunities and providing cost-saving benefits, especially when it comes to the maintenance of a fleet that is on a company’s balance sheet.
Expanding the usage of AI
Companies such as Girteka have been expanding the usage of AI in their operations, especially when managing the behind-the-scenes work behind every truck. For example, such a solution as TRAVIS, which is an online marketplace connecting various service providers, including parking, can save a lot of time for truck drivers, who have to find a place to rest for the night, alleviating a lot of pressure from them, since they know they will park a truck and get enough rest.
There are tools for planning and managing the fleet as well. Developed by Nexogen the Fleet Operator is the tool that optimizes an itinerary based on a number of factors, including arrival time, distance, and emissions, with the purpose-built model adapting to ever-changing road conditions.
Knowing that Girteka has over 6,400 trucks in its fleet, which have to be supported by employees at multiple bases and offices across Europe, that is a lot of data that has to be processed. As such, there are certain limitations that a duet of AI and a human can overcome, especially when changes happen quickly, like a car crash or a road blockade that severely limits the flow of traffic up ahead.
Optimizing processes can result in tangible benefits, including fuel savings and, subsequently, emissions, which is crucial, considering how much pressure regulators and other stakeholders are putting on the logistics industry, as well as other means of transportation, to reduce its environmental impact.
Nevertheless, the data that can be gathered and subsequently processed can also be used for other means. For example, in an article written by two authors from Vilnius Tech, a university in Vilnius, Lithuania, as well as Edvardas Liachovičius, the board member of Gireka, which analyzed freight rate and demand forecasting in the European road freight market, with the Netherlands – Italy lane being chosen as an example.
The three co-authors concluded that certain forecasting models perform better when predicting demand than others. At the same time, rate forecasting was challenging for the models, which examined 2021 and 2022, which included the pandemic and the war in Ukraine. As such, certain models provided poor results, while multivariate models performed much better with data input.
“Summarising this investigation, it can be stated that developed mathematical models can serve as supplementary tools in the decision-making process during the freight rate and demand forecasting process, complementing each other but not eliminating the necessity for human judgment.”
Driverless trucks
At the end of the day, one of the most precarious debates is around driverless trucks, which are slated to enter service sooner rather than later. After all, the aforementioned EP briefing already explored a future where HGV fleets, as well as other driver-dependent industries, would be replaced by autonomous technologies. However, the article stated that while AI can make workers’ lives easier, enable more people to enter the labor market, or cut operating costs, some jobs will be eliminated going forward.
That sounds like a complete 180 compared to the situation currently when the whole European market is struggling to hire enough drivers to meet demand, even if it has subsided in the past few months due to a difficult economic situation in the continent. Nevertheless, considering the cyclical nature of economies, it is more than likely that the industry will once again face a severe driver shortage, which will either be solved by more younger people joining the profession or the need for drivers will go down due to the inclusion of AI-based technologies within fleets.
The question then remains when the crossover point will come, considering that no automaker has successfully released an autonomous car, let alone a truck, which is much more complicated not only due to the latter’s mission and purpose on the road but also due to the safety concerns related to operating a truck.
Whatever the case might be, the future will come either way, with AI being at the center of attention of business leaders, scholars, and regulators alike, as the three main stakeholders will have to balance each others’ interests in order not to stifle innovation and at the same time, ensure that innovation can improve each others’ lives.
