You have only just wrapped your mind around the concept of big data. Now there is machine learning, deep learning, machine vision, neural networks, and artificial intelligence. What do these things mean? And, more importantly, what do they have to do with the business of transportation?
Quite a lot, as it turns out. In a recent survey of 433 senior executives in transportation, logistics, and supply chain industries conducted by Forbes Insights, 65 percent say their businesses are undergoing “tectonic shifts” driven in part by rapid advances in technology — specifically telematics, data mining, artificial intelligence, and machine learning.
What is Data Mining?
Data mining techniques are used to analyse large sets of data (sometimes referred to as “big data”) to reveal patterns, trends, and correlations. This exercise can often yield clues for how businesses can improve their performance. Many fleets, for example, comb through data on near collisions in an effort to predict and prevent future collisions. Others mine location and time-of-day data to find hot spots and reroute vehicles to avoid delays or collisions during certain times of the day.
How Data Mining Relates to Machine Learning
As telematic sensors proliferate in and around commercial vehicles, fleets are quickly collecting vast quantities of information, and data mining is one way to extract value from it.
But it is not the only way to leverage data. Increasingly, fleets are turning to machine learning to get even more out of their data and gain greater competitive advantages. These algorithms can be used to make faster, more intelligent decisions that can automatically optimise and deploy resources. Imagine a system that automatically assigns loads, adjusts schedules and routes vehicles based on real-time changes traffic, weather, customer requests, driver proximity, and other variables.
“Data mining looks to discover relevant information from a larger dataset,” said Stephen Krotosky, manager of applied machine learning at Lytx, “whereas machine learning is focused on designing algorithms to make predictions on the data. The two are intertwined, as the output of data mining is often used as the training data for machine learning algorithms.”
In other words, data mining can be used to develop smarter, more accurate algorithms that can “learn” from additional data.
Advancing Fleet Safety and Operational Efficiency
Machine learning can make short work of large amounts of data — serving up in seconds or minutes predictions and recommendations that would have taken days or weeks in the past. That is good news for anyone who has ever felt overwhelmed by the sheer volume of data being generated today from all corners of the industry.
But as advanced as machine learning has become, it has not come close to displacing humans. Human intuition continues to play a vital role in operations and strategy. Both data mining and machine learning are best leveraged in companies that combine them with human judgement.
At Lytx, for example, events are reviewed by professionals who know what to look for. Each event is combed by trained human eyes and tagged for potentially dozens of driving behaviours and conditions. “This analysis, combined with traditional telematics data, surfaces far more insights than machine analysis can alone,” Krotosky said.
Lytx clients are not the only ones to benefit from the combination of human and machine analysis. The company’s researchers, investigating the building of the next generation of video telematics technology, also stand to gain.
“The result [of this combination of human and machine analysis] is a rich dataset that can be leveraged to build models to detect and predict targeted tasks, such as mobile phone use,” Krotosky explained. “It can also be used to evaluate fuel usage, delivery efficiency, and so on.”
The ultimate goal of Krotosky and his team of researchers is not to develop cleverer algorithms. It is to create technologies that help fleets improve the safety of their drivers and advance their operational efficiencies to thrive in today’s ultra-competitive environment.
 “Logistics, Supply Chain and Transportation 2023: Change at Breakneck Speed,” Forbes Insights, 2018.