2/21/2017
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Rail News Home CSX Transportation 2/21/2017 Rail News: CSX Transportation
CSX Chairman and CEO Michael WardPhoto – CSX Corp.
CSX Chairman and CEO Michael WardPhoto – CSX Corp.
CSX Corp. announced today that Chairman and Chief Executive Officer Michael Ward and President Clarence Gooden will retire from the company effective May 31. Chief Sales and Marketing Officer Fredrik Eliasson has been appointed president effective Feb. 15, replacing Gooden, who will assume the role of vice chairman until his retirement.
Eliasson will maintain his current responsibilities in his new position. The changes are part of a senior leadership transition that CSX's board has been considering for more than a year, according to a company press release.
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Rail News Home Mechanical February 2017 Rail News: Mechanical
ByThis 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
ByThis email address is being protected from spambots. You need JavaScript enabled to view it. , Vice President of Content DevelopmentBig Data analytics promises to soon change railroad operations for the better. For years, financial and retail companies, among others, have been investigating how to collect, analyze, merge and leverage data to drive efficiencies and provide new services. Railroads are relatively new to the Big Data party, but they’re finding ways to leverage the mountains of data they’re collecting to better manage operations, and improve safety and security.In late 2016, Progressive Railroading attended the third annual Big Data in Rail Maintenance Planning conference in Newark, Del. Held Dec. 15-16, the conference is conducted by the University of Delaware’s Big Data Center and its Railway Engineering and Safety Program.What we learned: Railroads have deployed sensors. They’re monitoring field operations. They’re hiring data scientists. And the industry is learning to merge and analyze data to answer specific questions.It’s no easy task. But the couple hundred attendees in the college lecture hall — a mix of academics, railroaders, IT specialists and rail industry software providers — seemed eager to learn from each other and build on the discoveries.“Big Data finds the needle in the haystack,” said David Staplin, retired deputy chief engineer for Amtrak and chairman of the University of Delaware’s Railroad Advisory Board, in his introductory remarks for the conference.In 2014, railroads were dealing with a dearth of data, said Staplin. In 2015, they focused on how Big Data could improve safety, profitability and service, and they improved in those areas. But customers and the public want more, he said.Now, there is an abundance of data being analyzed to improve maintenance and maintenance planning.“We can use Big Data to find [the common truth] under the small, annoying points of weakness or failure in the system,” said Staplin. “Those small points might not result in failure individually or immediately, but analyzing the data points delivers patterns that lead to insight.”Of course, as with other scientific endeavors, investigation and analysis also can lead to more questions.“As we solve one problem with Big Data, we’re going to find other avenues to explore, to fix other problems,” said Staplin. “Eventually, Big Data will find the needle in the haystack. And if we don’t find and fix it first, our competitors will.”Railroad-specific needsAccordingly, the aim of Big Data in Rail Maintenance Planning 2016 was to focus on railroads’ specific needs and applications, said Allan Zarembski, research professor and director of the university’s Railroad Engineering and Safety Program. The conference presentations reflected that.Three years ago, “railroads were saying, ‘This is how many miles of track we have and here’s where we’re putting the sensors.’ Now, they’re discussing specific projects, results and challenges,” Zarembski said.“Modern railroads are making increasing use of new-generation track inspection and operating technology to obtain more and more data on the condition of track and equipment,” he added. “Since railways need to convert this data into usable information to help them plan their capital maintenance programs, there is a need for new and improved data analysis techniques.”In their respective presentations, representatives from BNSF Railway Co., Union Pacific Railroad, CSX, CN and Norfolk Southern Railway talked about specific project needs and goals, and what they hoped Big Data analytics could deliver.For example, CN and NS presenters took turns describing their efforts in a session titled, “The Use of Big Data to Evaluate Railroad Assets and Plan for the Future.” J. Shane Rice, assistant chief engineer - MW&S for NS, said he was working with his IT department to simplify the data generated from their track geometry car.“We want better analysis of current assets, and a better ability to plan repairs and predict failure,” said Rice. “This will help us become safer, more service-minded, and more productive and efficient.”CN Senior Manager of Engineering Technology Shaun Levandier told attendees that the Class I’s MOW officials are “using data to do simple analysis. We need to get better at combining data.” CN’s strength, he added, is a good geographic information system (GIS) designed network.“We want to make it easier for field folks to use the data. We want to better use the data for track assessment. And want to better manage data,” Levandier said. “CN is working to bring data sets together, and to combine systems, reports and scorecards to create an operational overview.”What Big Data can doMeanwhile, CSX officials are analyzing smarter onboard measurement systems, and they’ve identified 195 potential use cases for the data.“Sensors are at a price point where the potential to do better is there,” said Leo Kreisel, the Class I’s director of track testing. “There are enough machine vision systems [to choose from] and they are better. We see automated inspections supplementing human inspections. We want [sensors] on locomotives, for example, so the measurements can be continual.”CSX estimates that locomotive-based sensors will produce 250 gigabytes (GB) of data daily per locomotive. If sensors were on 300 of the railroad’s locomotives, the system would be generating 75 terabytes of data per day. (One terabyte equals 1,024 GBs of data, which is 472 hours of broadcast-quality video, 150 hours of high-definition recording or 2,000 hours of CD-quality recording.)Kreisel said CSX expects analysis of all that data to yield:
• Safer inspections, because they are machine-vision-based with virtual validation.
• Higher quality inspections, because more — and more objective — data will be generated.
• Reduced time for inspections, because of automation, so “maintenance of way doesn’t have to be maintenance in the way,” Kreisel said.Dwight Clark, general director of engineering technology for UP, also talked about how Big Data is enabling automated track condition assessment. And BNSF Director of Reliability Engineering David Friss discussed how his railroad is analyzing data to evaluate turnout performance. Other presentations referenced applications from the United States and abroad covering the use of Big Data in rail wear forecasting, rail joint management using automated inspection technology and the role of Big Data in risk management. Challenges of Big DataConference attendees also were able to dive deep into some of the challenges of combining, sorting and analyzing large data sets.In her keynote address, Transportation Technology Center Inc. (TTCI) President Lisa Stabler noted that “it’s very important that we discuss the [data] inputs, that we understand both statistical significance and practical significance. Without the technical understanding, spurious associations can be made.”No matter where the data comes from, analyzing it requires making a lot of decisions and assumptions. Stabler of TTCI, a research and testing organization that is a wholly owned subsidiary of the Association of American Railroads, explained that Big Data comes with three concerns that anyone who wants to make use of it must work through:• Quality. Understanding the quality of data is critical. Data quality can be affected by the frequency of data gathered over time, the quality of older data sets used for comparison and decisions made about data points that are extreme outliers. “Modern data collection amasses enormous amounts of data,” she said. “When using smaller data sets, you can use 95 percent accuracy. But with Big Data, you need 99 percent confidence that the data is accurate. [And] large data sets have their own problems.”
• Outliers. Big Data analysis does not necessarily mean the final results use all the data, said Stabler. That’s where decisions about outliers come in. That’s also where you have to know how your inputs are related.
• Auto-correlation. Big Data can have factors that are confounded or correlated over time (auto-correlated), she said.The railroad industry is beginning to work through these concerns for a variety of data sets. The goal is to be able to reliably apply algorithms to future data sets to predict, for example, wheel breakage, the need for tie replacement or other maintenance issues.Collecting data is comparatively easy. Analyzing the data to produce a useful outcome is time-consuming, but increasingly attainable. But the industry is still in the early phases of learning to use data analysis to reliably predict a specific outcome, rail data scientists say. So, many will no doubt meet again next year, at the fourth annual conference, to compare notes and learn more. next page
Keywords Browse articles on Big Data Internet of Things University of Delaware Amtrak BNSF Railway Co. Union Pacific Railroad CSX CN Norfolk Southern Railway Transportation Technology Center Inc. Contact Progressive Railroading editorial staff.
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• Safer inspections, because they are machine-vision-based with virtual validation.
• Higher quality inspections, because more — and more objective — data will be generated.
• Reduced time for inspections, because of automation, so “maintenance of way doesn’t have to be maintenance in the way,” Kreisel said.Dwight Clark, general director of engineering technology for UP, also talked about how Big Data is enabling automated track condition assessment. And BNSF Director of Reliability Engineering David Friss discussed how his railroad is analyzing data to evaluate turnout performance. Other presentations referenced applications from the United States and abroad covering the use of Big Data in rail wear forecasting, rail joint management using automated inspection technology and the role of Big Data in risk management. Challenges of Big DataConference attendees also were able to dive deep into some of the challenges of combining, sorting and analyzing large data sets.In her keynote address, Transportation Technology Center Inc. (TTCI) President Lisa Stabler noted that “it’s very important that we discuss the [data] inputs, that we understand both statistical significance and practical significance. Without the technical understanding, spurious associations can be made.”No matter where the data comes from, analyzing it requires making a lot of decisions and assumptions. Stabler of TTCI, a research and testing organization that is a wholly owned subsidiary of the Association of American Railroads, explained that Big Data comes with three concerns that anyone who wants to make use of it must work through:• Quality. Understanding the quality of data is critical. Data quality can be affected by the frequency of data gathered over time, the quality of older data sets used for comparison and decisions made about data points that are extreme outliers. “Modern data collection amasses enormous amounts of data,” she said. “When using smaller data sets, you can use 95 percent accuracy. But with Big Data, you need 99 percent confidence that the data is accurate. [And] large data sets have their own problems.”
• Outliers. Big Data analysis does not necessarily mean the final results use all the data, said Stabler. That’s where decisions about outliers come in. That’s also where you have to know how your inputs are related.
• Auto-correlation. Big Data can have factors that are confounded or correlated over time (auto-correlated), she said.The railroad industry is beginning to work through these concerns for a variety of data sets. The goal is to be able to reliably apply algorithms to future data sets to predict, for example, wheel breakage, the need for tie replacement or other maintenance issues.Collecting data is comparatively easy. Analyzing the data to produce a useful outcome is time-consuming, but increasingly attainable. But the industry is still in the early phases of learning to use data analysis to reliably predict a specific outcome, rail data scientists say. So, many will no doubt meet again next year, at the fourth annual conference, to compare notes and learn more. next page
Keywords Browse articles on Big Data Internet of Things University of Delaware Amtrak BNSF Railway Co. Union Pacific Railroad CSX CN Norfolk Southern Railway Transportation Technology Center Inc. Contact Progressive Railroading editorial staff.
Feb
20
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.
previous page next page 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.—
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20
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
previous page next page 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
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Browse articles on U.S. Department of Transportation Federal Railroad Administration Big Data 2016 Big Data in Railroad Maintenance Planning conference
Feb
20
The job of a data scientist involves poring through huge quantities of often disparate data to find insights that may prove helpful in every aspect of a business, including marketing, logistics and human resources. It also includes cleaning data, dealing with gaps and sifting through incomplete or poor definitions. But, according to data consultant Thomas C. Redman, “the best data scientists get out and talk to people.”
Known as “the Data Doc,” Redman helps companies improve data quality. In a January 26, 2017, Harvard Business Review article, he described the challenges of adding context and assessing the quality of the sensor streams and images encompassed by Big Data.
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2/17/2017
Rail News: Supplier Spotlight
Feb
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Rail News Home Union Pacific Railroad 2/17/2017 Rail News: Union Pacific Railroad
Union Pacific Railroad was named the most admired among trucking, transportation and logistics companies by Fortune for the seventh consecutive year, the Class I announced yesterday.The designation also marked the 14th time in 18 years the company has been named No. 1, according to a UP press release.UP ranked first in eight performance areas identified by Fortune: innovation, people management, use of corporate assets, social responsibility, quality of management, financial soundness, long-term investment value and quality of products or services.The Korn Ferry Hay Group, Fortune's research partner, surveyed executives, directors and analysts to select companies they most admired from a list that began with 1,500 international and domestic companies."This recognition is a testament to Union Pacific's more than 40,000 employees, who commit daily to working safely and efficiently for the good of our customers, communities, shareholders and their fellow employees," said UP Chairman, President and Chief Executive Officer Lance Fritz.The magazine's annual "most admired" list will be published in the March edition. Contact Progressive Railroading editorial staff. More News from 2/17/2017
Union Pacific Railroad was named the most admired among trucking, transportation and logistics companies by Fortune for the seventh consecutive year, the Class I announced yesterday.The designation also marked the 14th time in 18 years the company has been named No. 1, according to a UP press release.UP ranked first in eight performance areas identified by Fortune: innovation, people management, use of corporate assets, social responsibility, quality of management, financial soundness, long-term investment value and quality of products or services.The Korn Ferry Hay Group, Fortune's research partner, surveyed executives, directors and analysts to select companies they most admired from a list that began with 1,500 international and domestic companies."This recognition is a testament to Union Pacific's more than 40,000 employees, who commit daily to working safely and efficiently for the good of our customers, communities, shareholders and their fellow employees," said UP Chairman, President and Chief Executive Officer Lance Fritz.The magazine's annual "most admired" list will be published in the March edition. Contact Progressive Railroading editorial staff. More News from 2/17/2017
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2/17/2017
Rail News: Union Pacific Railroad
Feb
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2/17/2017
Rail News: Union Pacific Railroad
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2/17/2017
Rail News: Passenger Rail
Feb
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Rail News Home CSX Transportation 2/17/2017 Rail News: CSX Transportation
Mantle Ridge LP responded yesterday to CSX's call for a special shareholders' meeting to discuss the hedge fund's proposals, which include installing former Canadian Pacific Chief Executive Officer E. Hunter Harrison as the next CEO of CSX.Mantle Ridge CEO Paul Hilal wrote to CSX's board to comment on a CSX press release reported earlier this week that described the state of negotiations between the parties and the railroad's call for the special meeting. In his letter, Hilal took issue with some of the railroad's views of certain issues discussed as part of the negotiations.CSX responded last night by saying the board will carefully review Hilal's letter. "As demonstrated by our recent actions, the CSX board of directors is always willing to engage in constructive dialogue with our shareholders and to consider their views on our company's business and strategy," CSX's latest press release stated. Contact Progressive Railroading editorial staff. More News from 2/17/2017
Mantle Ridge LP responded yesterday to CSX's call for a special shareholders' meeting to discuss the hedge fund's proposals, which include installing former Canadian Pacific Chief Executive Officer E. Hunter Harrison as the next CEO of CSX.Mantle Ridge CEO Paul Hilal wrote to CSX's board to comment on a CSX press release reported earlier this week that described the state of negotiations between the parties and the railroad's call for the special meeting. In his letter, Hilal took issue with some of the railroad's views of certain issues discussed as part of the negotiations.CSX responded last night by saying the board will carefully review Hilal's letter. "As demonstrated by our recent actions, the CSX board of directors is always willing to engage in constructive dialogue with our shareholders and to consider their views on our company's business and strategy," CSX's latest press release stated. Contact Progressive Railroading editorial staff. More News from 2/17/2017
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Rail News: CSX Transportation
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Rail News: CSX Transportation
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Rail News Home BNSF Railway 2/17/2017 Rail News: BNSF Railway
The Illinois attorney general announced a settlement with BNSF over a 2015 crude-oil train derailment in Galena, Ill.Photo – BNSF.com
The Illinois attorney general announced a settlement with BNSF over a 2015 crude-oil train derailment in Galena, Ill.Photo – BNSF.com
Illinois Attorney General Lisa Madigan has announced a settlement has been reached with BNSF Railway Co. regarding a 2015 train derailment in Galena that spilled a large amount of crude oil, which threatened to contaminate groundwater and nearby surface water.
Under the terms of the settlement entered in Jo Daviess County Circuit Court, BNSF will pay $50,000 in civil penalties, Madigan said in a press release issued Tuesday.