Machine Learning And Big Data In Transportation

Nowadays, machine learning and big data are closely related. They are even interdependent. Many industries regularly use these modern solutions to grow and achieve their business goals. For some companies on the market, the use of ML and BD becomes even mandatory. These include, in particular, organizations from the transportation industry.

Modern technologies and intelligent solutions are changing the way companies deliver both passengers and goods. Both the people who use the services of transport companies and the enterprises themselves want transport to be faster, cheaper, more efficient and have as little impact on the environment as possible. All areas of transportation currently use machine learning and big data solutions. Land, sea, or air transport… Entities operating in these industries often use the services of Machine Learning Consulting to use the latest artificial intelligence solutions effectively. Let’s check how and what benefits they get from it.

Machine learning and big data: Two important disciplines

Before we go to the examples, let us remind you what ML and big data are and the correlations between these concepts.

Machine learning is an AI (artificial intelligence) branch and technique. It is about solving complex statistical and data exploitation problems by recognizing repeating patterns in multiple data streams. So put, and it is up to the computer to perform predictive analysis based on statistical techniques. Then, within a few seconds, the machine performs data mining and detects unusual or suspicious behavior, such as fraud.

Big data can be defined as data sets that can be collected and analyzed to obtain valuable strategic information for companies. This data can also be used in machine learning projects. Big data can serve several purposes: accelerating decision-making or processing of exhaustive data ranges.

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Links Between Machine Learning And Big Data

Machine learning is based on big data. Indeed, a machine cannot develop its intelligence with this technology without large data sets. The larger you can find the amount of data, the more accurate a solution to the problem. These two concepts work together. Big data is a condition for the functioning of machine learning. So let’s see how this cooperation contributes to the development of transportation.

Machine learning and big data in various kinds of transportation

According to Prescient & Strategic Intelligence’s AI in Transportation Market Overview, AI enhancements are predicted to add up to $ 13 trillion to a global economic performance by 2030. Therefore, there is no doubt that the transportation industry is trying to make the most of the potential of AI, and thanks to this, the fundamental growth dynamics are noticeable. To study this topic, let’s take a closer look at the different types of transport.

Land Transport And The Problem Of Traffic Jams

Traffic jams are a real scourge of the present day. Millions of lost hours, delayed and nervous drivers and disproportionately high economic losses are just some of the daily challenges of road transport companies. Road infrastructure is often inadequate or in poor condition, so traffic jams are a problem practically worldwide. There is no doubt that any delay in transport can harm entire supply chains and disrupt global logistics. Intelligent solutions that, using machine learning and big data, can help you manage the road infrastructure with help.

An example of the use of AI in road transport is the city of Boston, which has created a mobile application called Speed Bump to support traffic on their roads. This application allows users to collect and upload road data while driving. The city of Boston then uses the collected data to find and solve current road problems and plan long-term infrastructure improvements.

Air Transport And The Problem Of Delays

Most likely, you will agree that air transport should be free from all kinds of delays, which are particularly painful and expensive in this type of transportation. For example, according to a study by scientists at the University of California at Berkeley, the indicative costs caused by flight delays in the US are $ 39 billion. But, of course, financial losses are not the only consequence of delayed flights. Furthermore, you should bear in mind that the aviation industry must also consider customer experience and the efficiency of the supply chain, which delays are affected too.

Machine learning and big data have been successfully preventing potential delays for several years. Experts in artificial intelligence create complex algorithms that, by analyzing historical data and those available in time, can arrange the schedule so that no procedures cause downtime. AI solutions also enable timely planning of appropriate maintenance and repair activities.

Sea Transport And Port Management

Artificial intelligence is very often used in unexpected areas. The maritime transport sector is a prime example. There are countless uses of artificial intelligence: optimization of port logistics, species protection, including autonomous navigation and environmental protection. In addition, marine data collection has increased significantly in recent years due to sensors, radar, sonar, underwater robots, and marine drones.

Companies, public authorities, and non-governmental organizations create databases and advanced algorithms, among others, for the effective management of port and maritime logistics. The infrastructure has to adapt to the increase in container flows and the size of the vessels. New technologies – including AI – are now making it possible to respond to port challenges, from the back office to predictive logistics and execution. Seaports’ effective organization and management are increasingly optimized thanks to robotics, autonomous vehicles, computer vision, and conversation through interfaces.

Machine learning and big data open up new possibilities in the transportation

The above-described ML and big data are just a few examples of the importance of artificial intelligence in today’s transportation realities. However, they illustrate how many modern solutions can bring to any transport industry and why companies worldwide increasingly use machine learning consulting services. Issues in artificial intelligence, managing massive data sets, or machine learning are certainly not easy and require expert knowledge, advanced technological solutions, and often the involvement of the entire internal team. Therefore, it is worth using the services of consulting companies employing the most eminent specialists who help implement revolutionary ML tools and big data in organizations related to transportation.

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