IoT Sensor Data Fueling Interest in AI

“If you want a truck to go from one place to another through Manhattan, for example, there are many possible routes,” Kirkpatrick said. “It’s very difficult for a human to look at all the different permutations and figure out the most efficient way to cross Manhattan, given traffic, construction and other factors. The power of machine learning is that you can take in data from all these different crossings and variables, and make instant correlations between all of these data points in a way that humans just can’t do, and even regular data analytics can do.”

Transportation and Logistics Use Cases for AI

Transportation and logistics sectors present AI with a multitude of potential use cases, as they are industries that not only rely on complex machines, but also often follow strict timetables and rely on full capacity usage to achieve maximum value.

In the transportation industry alone, Tractica forecasts AI revenue in seven separate use cases:

1. Prediction of traffic density.
2. Predictive maintenance.
3. Vehicle network and data security.
4. Sensor data fusion in machinery.
5. Weather forecasting.
6. Machine/vehicular object detection/identification/avoidance.

Localization and mapping. In logistics, it also forecasts for the latter three of those use cases, in addition to supply chain and logistics; demand forecasting for warehouse and supply chain; and satellite imagery for geo-analytics.

Among all of these, machine/vehicular object detection/identification/avoidance for the logistics sector, which would cover things like Kirkpatrick’s autonomous trucking example, is anticipated to be the biggest revenue-generating use case area, reaching more than $584 million by 2025. Meanwhile, in transportation, the top use cases are expected to be predicting traffic density and predictive maintenance, with the former home to $439 million in AI revenue by 2025, and the latter reaching $315.2 million by 2025.

Predictive maintenance was central to IBM’s February unveiling of its Maximo Asset Performance Management suite, what it described as a package of IoT solution that leverages AI, to “collect data from physical assets in near real time and provide insights on current operating conditions, predict potential issues, identify problems and offer repair recommendations.” the company said at the time. The Metropolitan Atlanta Rapid Transit Authority already is working

Meanwhile, in logistics, IBM and logistics giant DHL teamed up in 2018 to produce a whitepaper that outlines several potential AI applications in that particular sector. One of those focuses on predictive network management for air freight lanes. DHL created a machine learning-based tool that analyzes 58 different parameters of internal data to predict up to a week in advance if the average daily transit time for a given lane will rise or fall. If air freight planners are able to predict air freight transit time delays, they can proactively attempt to mitigate the expected delays and make more informed decisions about which airlines should carry their freight, DHL said in the white paper.