Machine learning in the transportation industry

When we look at the present technologies used in various industries, machine learning in the transportation industry can be seen as the future. Modern-day technologies, such as RPA, AI, or machine learning will be a great help in any kind of industry due to their capacity to give more time to the employees for their personal development.

First, let us see what machine learning is.

According to Wikipedia, machine learning is the study of computer algorithms that improve automatically through experience. Machine learning uses algorithms to build a model based on data in order to make predictions or decisions that don’t involve human intervention and programming. These algorithms are used in a variety of applications where conventional algorithms are not enough to perform the needed tasks.

Machine learning can be approached in 3 different ways:

Supervised learning – this method implies the presentation of example inputs and their desired outputs to a computer with the main goal being to learn a general rule that maps inputs and outputs.

Unsupervised learning – the algorithm has no examples to learn from, it is left on its own to find structure in its input. This serves as a goal itself and a means toward an end.

Reinforcement learning – this implies that the computer interacts with a dynamic environment having to perform a specific goal, for instance, driving a vehicle, filling in data, playing a game, etc.

As for the benefits of machine learning, it stands as a pillar for continuous process improvement, automation of decision-making tasks, it can identify trends and patterns and is applicable to a wide range of applications.

Is machine learning able to help the transportation industry?

The short answer is yes. Machine learning had great applicability in the transport industry. In recent years, ML techniques have become a part of smart transportation. Through deep learning, ML explored the complex interactions of roads, highways, traffic, environmental elements, crashes, and so on. ML has also great potential in daily traffic management and the collection of traffic data.

Machine learning can also help back-office operations as well. Let’s take, for instance, a transport company. Daily, they can receive dozens if not hundreds of orders, depending on how big the company is. Imagine that all those transport orders are manually processed. These operations take a huge amount of time to do it and also is considered to be a boring and error-prone task. To ensure the flow of the transports, the order processing must be done at a certain time. Good thing that this is a process that can be easily automated with the combined technologies of RPA and machine learning.

The solution for the automated processing of transport orders has a few steps. First, the document must be uploaded into a program from where the bot can pick it up. Second, the document is read and classified. Third, data is extracted and placed accurately into fields. Last, the document is exported.

However, the documents vary in shapes and layout, have insufficient data, or need human intervention. RPA, combined with machine learning, can create a learning process that will generate accurate data, fill in the documents while optimizing time, eliminating the need for human intervention for good.

The result of implementing this kind of solution would be decreasing the processing costs, increasing employee satisfaction, high-quality results, and a more agile company.

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