How IoT Will Transform Transportation

The power of data to improve performance and customer experience.

By Chris Pemberton
May 02, 2023

This article is the fifth in a series exploring use cases and innovative approaches to edge computing and IoT across a range of industries, where these technologies are expected to deliver outsized business value.

According to Research and Markets, the global market for the Internet of Things (IoT) in the transportation industry is projected to reach nearly $496 billion by 2030, up from about $83 billion in 2020. That's a compound annual growth rate of nearly 20% during this decade.

Factors fueling this growth include near-ubiquitous internet connectivity in many parts of the world and a reduction in the cost of powerful sensors and controllers. Advances in information and communications technology are enabling rapid adoption of cloud computing and analytics. All of this will come together to provide lucrative opportunities in the coming years.

IoT solutions promise to make transportation organizations smarter and more successful. IoT is at the core of forces reshaping transportation: providing greater safety; making travel more efficient; improving vehicle, vessel, and aircraft maintenance; and building more strategic traffic management.

Data is the key to IoT value. Strategically placed sensors on trains, planes, ships, and cars capture operational data that can be analyzed at the edge, in near-real time, to prevent accidents, identify worn parts before they go bad, and make routing adjustments based on weather and traffic conditions. But data has even greater value when it moves to the cloud, where you can aggregate data from many sources, identify patterns, and make artificial intelligence (AI) models more efficient and accurate.

Unfortunately, most data that is generated at the edge stays at the edge — 99% of it is never analyzed. That is a lot of lost value. The transportation industry is figuring out how to extract value from its data and many businesses are using local edge devices to sort and analyze data needed for real-time, low-latency applications and then move that data to the cloud for further analysis and possible monetization. Here are some real-world examples of how the transportation industry is investing in IoT for business impact.

Railroads: Connected trains keep operational performance on track

The railroad industry is replete with IoT use cases that improve passenger experience, on-time delivery of goods, safety, and reliability.

Deutsche Bahn Group (DB Group) is a German rail transport company that is digitizing almost every aspect of its operations to improve business performance and customer experience. DB Group is working with partners, such as Intel, Amazon Web Services, and IBM to harness IoT for customer value. For example, Intel works with DB Cargo to monitor assets and coordinate maintenance operations. They gather massive amounts of data to analyze it near the edge and then forward relevant data to the cloud for analyses, ranging from simple operational statistics to machine learning and AI models to improve future operations.

IBM is working with DB Group to improve the efficiency of both passenger and cargo trains, and it has designed and implemented huge "data engineering pipelines" that receive sensor data, format it, enrich it with context from other data sources, and feed it back to the operational systems in near–real time to enable operational improvements. For example, sensors on a train door can collect detailed data about how many times it opens and closes, how much power it takes to operate, and how fast it opens and closes. When this data is sent to the cloud and combined with data from countless other doors, AI models can identify when a door is starting to fail and order a replacement before that happens.

Trucking: AI drives efficiency in a fragmented market

Nearly three-quarters of U.S. freight by weight is moved by trucks. J.B. Hunt Transport aims to make that process more seamless by becoming the most efficient transportation network in North America. One way it is doing this is by offering dynamic freight matching, which connects a business's specific shipping needs with available carrier capacity. Its system considers details such as price, weight, and location, and it must deal with a fragmented carrier market of more than 3 million drivers. But the company's legacy information architecture, rapid growth in data, and limited AI capabilities could not keep up. Its systems struggled to process and store the massive volume of data generated from location pings every 15 minutes from hundreds of thousands of loads being moved. They needed a real-time data solution and a robust AI platform to build a matching system that took into account all that fragmented data.

The new platform enabled J.B. Hunt to save $2.7 million in infrastructure costs and data science productivity.

J.B. Hunt needed to unlock the value of data that was trapped in its legacy data warehouses. The company worked with Databricks and Google Cloud to develop a business intelligence and AI platform — Databricks Lakehouse — that could capture this data and support real-time analytics for data engineers, scientists, and others across the business.

The new platform enabled J.B. Hunt to save $2.7 million in infrastructure costs and data science productivity. Because data and intelligence have been moved to the cloud, the new system can train thousands of machine learning models in less than four hours. Such models deliver freight recommendations to carriers 99.8% faster than before.

Shipping: Smart containers provide supply chain visibility

Shipping has many of the same challenges as rail transport, but on a global scale. A.P. Moller-Maersk, for example, runs a fleet of more than 700 vessels and transports more than 12 million containers annually — and it all needs to be tracked and monitored across the oceans and in more than 130 countries. Additionally, the Danish shipping giant operates container terminals around the world, with cranes, trucks, and other hardware all relying on connections to the internet.

The company uses IoT to remotely control humidity and temperature within its containers and to provide customers with continuous visibility into the location and condition of its goods. It can also track the location of empty containers and efficiently move them to where they are most needed. Cloud-based services offer IoT security and make it easier for the customer to get data faster. The company has even developed a mobile app to give customers instant access to information about their shipments.

A.P. Moller-Maersk is working with Onomondo to develop its IoT technology based on Microsoft's Azure cloud platform. By fully integrating end-point devices with the network and cloud, the company simplifies and streamlines communications and strengthens security.

On the edge of transformation

The transportation industry is on the cusp of creating transformational change by harnessing the power of data that has been sitting unused in edge devices or data centers. As data scientists build out AI models, they need large volumes of data to validate, refine, and extend AI capabilities. IoT provides that data.

By unlocking their unused data, companies will develop new business and revenue models that they can use to compound its value with new AI products, which they can then sell to other companies. For example, autonomous car makers can use AI to identify traits of good driving and sell those models to insurance carriers, who can then offer discounts to customers with those driving habits. Using data analysis, shipping companies can use — and sell — container-management models that help them minimize the number of empty containers they transport. In every segment of transportation, operational data from many sources can be aggregated, analyzed, and turned into predictive maintenance models.

IoT can make transportation smart. When you add the storage, security, and analytical power of the cloud, the opportunities for transformation have only just begun.

Learn more about how to activate edge and IoT data at scale for use in any cloud environment.

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