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Rail News Home Mechanical February 2017 Rail News: Mechanical

By This email address is being protected from spambots. You need JavaScript enabled to view it.The last year and a half hasn’t been very kind to rail-car fleets or their owners, particularly the leased fleets. Carload volumes have been depressed in most market segments. Lessees have been returning cars. Fleet surpluses have jumped to levels not seen since the Great Recession. Cars are stored everywhere. And in too many cases, the best thing lessors can say about a lease rate is that they actually have one. Almost every metric you can look at to gauge rail equipment industry health has been challenged.So why have rail cars been so attractive to investors over the past couple of years? And why are the values of used rail cars as high as they are?The simple answers are that rail cars retain their values over time better than most people realize, long-term rail fundamentals are strong, and there are a lot of investors that want to take advantage of it.Currently, the North American fleet totals a little over 1.6 million rail cars, of which about 850,000 are owned by leasing companies. There is an active secondary market for used rail cars sold among leasing companies.In the past, a new investor or two might enter the market every few years. Or one lessor might acquire another. But the pace of entry and exit into the rail equipment marketplace has increased significantly over the past three years. From 2014 through 2016, almost one-third of all rail cars owned by leasing companies changed ownership! While some existing leasing companies got bigger, more than 10 new ones launched during this period — collectively, they have a lessor ownership share that is approaching 10 percent of the market. Meanwhile, at least eight existing lessors divested their fleets and exited the market.Outlook for 2017: It’s fairly positiveThere are a lot of reasons companies enter or exit the market. Most of the lessors that elected to exit in recent years did so because it was time and the price was right. However, for a couple of these companies, there also were strategic considerations in their decisions to divest. In addition, most lessors routinely sell assets in an effort to make a profit on longer held assets or to readjust their portfolios.On the investor side, the rationales are a little more varied. Some of the larger strategic players have decided to double-down and have been aggressive buyers of rail cars. Other smaller players are acquiring for scale, and leveraging their market presence and expertise.As for the new market entrants, the main rationale has been a desire to find a new asset class in which to deploy capital. Some are already in the equipment leasing business and rail cars are just a new asset type. For others, however, the motivation is to invest in a new operating platform that will provide earning diversification that either complements, or hedges against, their other portfolio companies.The one thing all the players have in common is their positive long-term outlook for rail equipment. New investors quickly learn what the existing players already know: that despite the inevitable market fluctuations, rail cars are long-lived assets that are relatively easy to remarket, compared with other assets classes; have a large, reliable and creditworthy customer base; and over the long run provide good returns and retain their market values.Rail equipment values historically have been strong, mainly due to the attractive earnings and cash flow these assets generate, and the long-term stability of the markets they serve. While some may view the addition of new investors as a threat to their established market positions, the new players are actually their secondary market customers and provide a valuable outlet for larger fleet owners to monetize portions of their fleets.Going forward, these newer lessors will continue to grow their fleets. Equipment values will probably remain higher than if there weren’t as robust a new owner class as there is today. The market outlook for 2017 is fairly positive vs. 2016, so many of the fleet metrics will improve and help close the gap between fleet earnings performance and equipment values. And some of these new lessors/buyers actually may be sellers in the next consolidation cycle.This email address is being protected from spambots. You need JavaScript enabled to view it. is senior vice president and chief commercial officer of AllTranstek LLC, a private transportation consulting company that provides fleet management, technical and strategic consulting to the rail industry. In conjunction with FTR Intel, Dick forecasts the rail equipment markets for a broad client base.
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Rail News Home MOW February 2017 Part 1 : IoT: The rail industry is learning to analyze data to answer specific MOW questions Part 2 : Sidebar: Embrace data to improve safety, FRA says Part 3 : Sidebar: Big Data - A few definitions Part 4 : Sidebar: How to think like a data scientist Rail News: MOW

At the 2016 Big Data in Railroad Maintenance Planning conference, Gary Carr, chief of the Track Research Division of the Office of Research and Development for the U.S. Department of Transportation, Federal Railroad Administration (FRA), urged attendees to “embrace the data to improve safety and prevent derailments.”FRA’s Big Data R&D projects encompass large databases of track geometry inputs, wheel force data and more, combined with image processing, neural network and machine learning technologies, Carr said.FRA’s knowledge and skill is evolving just as the railroads are, and its projects reveal the challenges and the promise of Big Data.“Our early neural net system memorized the track security inputs well, but it didn’t interpret the real-world data well, and it didn’t predict anything,” Carr told attendees of the conference, held Dec. 15-16, 2016, at the University of Delaware in Newark, Del. “The second version of the neural net did a good job of learning the rules associated with the data.”In one project, FRA researchers inspected 50 miles of track. They recorded 15,000 “system recognitions” or images that resulted in 5,000 “boxed recognitions” or inspection areas to be further investigated. Analysis resulted in 500 possible “defects.” Ten of those possible defects were selected to receive additional hand tests, resulting in five hand-verified defects and five remedial actions taken. Then, a broken rail derailment occurred at the site of one of the other 500 defects identified.Said Carr: “Now the question is: Can we modify our processes to train neural nets to get the answers we need? A training data set plus a cross-validation data set equals machine-learning algorithms that can significantly improve the process.”— Renee Bassett

Rail News Home MOW February 2017 Part 1 : IoT: The rail industry is learning to analyze data to answer specific MOW questions Part 2 : Sidebar: Embrace data to improve safety, FRA says Part 3 : Sidebar: Big Data - A few definitions Part 4 : Sidebar: How to think like a data scientist Rail News: MOW

Data analysis requires context, and data scientists likely will be coming to railroaders to get that context. Here are some terms they might hear.“Data analytics” can be separated into quantitative and qualitative data analysis. The former involves analysis of numerical data, quantifiable variables and comparing or measuring those values statistically. The qualitative approach is more interpretive. It seeks to understand the content of non-numerical data like text, images, audio and video.Railroads have long been involved in an advanced type of data analytics called machine learning. This artificial intelligence technique, often applied to locomotives, uses automated algorithms to churn through data sets more quickly than data scientists can do via conventional analytical modeling. Other advanced types of data analytics include data mining, which sorts through large data sets to identify trends, patterns and relationships; and predictive analytics, which seeks to predict customer behavior, equipment failures and other future events.Big Data analytics, which is the next advance in the science, applies data mining, predictive analytics and machine learning tools to sets of big data that often contain unstructured and semi-structured data.— This email address is being protected from spambots. You need JavaScript enabled to view it.