When famous Shark Tank investor Kevin O’Leary was asked by his son why he didn’t invest in Tesla, he said he was not interested in investing in the car company and was instead interested in shorting it. To hear him tell it then, “I love the car; now the stock is crazy. Why would you pay that multiple for a company that makes no money?” Today, he proudly owns shares in the company and loves to tell the story of how he was won over.
The story is important because it demonstrates two things: (1) how technology is driving the entire transportation sector, and (2) how policymakers and regulators need to revise their perspectives to keep pace.
Tesla is not just a vehicle company, it is a data technology company. Why? Every mile that each car drives records data that makes that car and future cars better. Whenever a Tesla drives its standard route during the day and then comes back and plugs in, it uploads the data it acquired throughout the day. O’Leary explained further that, “Every car is reporting back to headquarters, making the resolution of the dataset better and better and better.” Every road interaction, surface condition, all of the dynamic activity, all coded into a smarter map and more sophisticated computer so the car can think better the next time it is on the road. This improves operations like autopilot and eventually fully autonomous operation.
On the road, where interactions are often unpredictable and dynamic, data is essential. But this type of continuous data collection is not limited to Tesla nor even to roadways. In fact, the railroad industry has pioneered its own data collection technology, signaling the next great shift in the evolution of rail.
Just as O’Leary and others once viewed Tesla as simply a car company, many view rail as a relic of the industrial revolution. Many think of trains as bulky, screeching machines burning fossil fuel to muscle down long stretches of track. But today, trains are technology-fitted powerhouses equipped with sensors and computers and tied into a network of other technology on the ground, at rail yards, and even on satellites.
From coal-fired steam engines to diesel and now electric trains, enormous shifts have occurred on the rails. Alongside these developments have been companion technologies to make operation more efficient and safer, but in recent years, it is data that has taken a central role. Policymakers must account for these developments and realign their approach accordingly.
Just like Tesla, trains are now capable of continuous, autonomous data collection that can send streams of information to human operators and artificial intelligence for analysis to improve safety and efficiency. Through autonomous track inspections, equipment can now be mounted directly onto locomotives or railcars so that every mile the train travels serves as its own inspection, data collection run, and digital pin drop for maintenance follow-ups.
With laser precision, technology can inspect track in real-time, measuring and detecting track spacing and micro-deficiencies that human inspectors may not even be able to see. This also allows the inspection and data collection to happen in real-world conditions of a fully-loaded freight train moving along the track, rather than a human walking the track or a retrofitted inspection truck mounted on the track. It also allows the inspection while the ordinary course of business is still conducted, not sacrificing time and resources to delay trains or block sections of track for alternative inspection vehicles – this is essential today as supply chain crunches require efficient freight movement.
The Autonomous Track Geometry Measurement System (ATGMS) is just one form of this technology that assesses at least seven elements of track geometry to provide a comprehensive picture of track conditions, maintenance needs, risk analysis, and safety information. This quality assurance tool is also a safety asset and allows a constant relay of information to key decision-makers.
A host of other technology on trains today allow remote monitoring to be done continuously, autonomously, and with precision. Added elements like Positive Train Control (PTC) help human operators and allow trains to communicate with other trains, infrastructure, and even global positioning systems. More data points enable rail carriers to conduct their business more safely and efficiently. To get more data, certain regulatory roadblocks need to be revisited and hurdles to greater deployment should be flattened. Extensions on pilot programs, permanent waivers for testing new technology, and performance-based regulation should be considered.
Every bit of track and rail data collected is an asset to individual carriers and the entire industry. Encouraging additional rollout of autonomous track inspection technology will solidify the next major shift in rail, taking it out of the realm of an industrial relic and placing it firmly into its rightful place as a data and technology sector that safely moves over a billion of tons of raw materials and finished goods every year. When Kevin O’Leary opened his eyes to a new perspective, he saw the potential of data in the transportation industry. Today, regulators must recognize this same reality for rail and promote data collection by the industry.
Written by Benjamin Dierker, Director of Public Policy
The Alliance for Innovation and Infrastructure (Aii) is an independent, national research and educational organization. An innovative think tank, Aii explores the intersection of economics, law, and public policy in the areas of climate, damage prevention, energy, infrastructure, innovation, technology, and transportation.