Port Technology: Going Green with AI

Press Review

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Artificial Intelligence (AI) is seemingly popping up everywhere these days. As a niche AI system provider, INFORM has been delivering AI solutions for just over two decades, and we have found that within the maritime sector, optimization modules deliver value across terminals. This is because as decisionmaking is improved, efficiency is realized across the terminal in both the short- and long-term. Short-term savings often translate directly to the bottom line. Efficiencies such as reductions in vehicle travel, crane travel, and yard re-handles are the easiest to see and calculate. Long-term savings, as well as potential revenue increases, can be harder to see initially, however.

Reductions in overall handling equipment as well as delaying or avoiding new equipment purchases happen slowly as operators adjust operational procedures to maximize the efficiency gains possible. Furthermore, increasing yard, truck, and rail handling capacities are intangible results that are the byproducts of more efficient terminals. All of these long-term outcomes deliver significant value, and through these activities, there are also strong environmental sustainability outcomes that can be achieved simultaneously. This paper focuses on one optimization module, INFORM’s Train Load Optimizer, and forms a case study for how strong optimization can deliver cost savings while also improving environmental performance.


It shouldn’t come as a surprise that rail has been growing since the mid-2000s and is forecasted to continue growing well into the 2020s. Rail is the most efficient way to transport goods over land. When compared to truck transportation, rail is 3.5 times more efficient averaging just over 184 tonkm/l (480 ton-mi/gal) of fuel consumption compared to around 42 ton-km/l (110 ton mi/gal) for trucks.

The subsequent growth in rail volume has challenged our industry to improve the way we optimize train load planning. The most basic measure considered when planning a train is slot utilization. This applies for both single and double stacked trains. However, as was well articulated in the 2005 research paper, “Options for Improving the Energy Efficiency of Intermodal Freight Trains” by Lai and Barkan1, slot utilization is only the most basic measure. Two trains can be identical in slot utilization (i.e., number of loaded containers) but present very different loading patterns and aerodynamic resistance.

To keep this paper legible to the nonmathematicians out there, essentially the “Davis Equation” developed by W.J. Davis in 1926 laid the foundation for measuring the resistance forces on a train. It has been modernized multiple times to account for new equipment and track structure, but its foundation holds true. It outlines three variables that impact a train’s resistance: bearing resistance, flange resistance, and aerodynamic resistance. In the Lai and Barkan study, they identified that for intermodal trains traveling at 60kph (35mph) aerodynamic resistance becomes the single largest factor which impacts a train’s resistance (see Figure 1).

Figure 1 - Train Resistance Variables Impact at Speed

Figure 1 - Train Resistance Variables Impact at Speed


While Lai and Barkan outline several factors that impact the individual elements of aerodynamics, the paper ends up focusing on one in particular – the size of the space between containers on adjacent railway cars, or, what they term as “Gap Length.” In short, the smaller the Gap Length, the more aerodynamic a train is. In addition, smaller Gap Lengths at the front of the train have the largest impact on the trains overall aerodynamic efficiency. As an example, a Gap Length of 1m (3ft) produced approximately 33% less aerodynamic resistance compared to one that was 4m (12ft) in length.

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