We have built the KONUX Predictive Maintenance System for Rail Switches, the first AI-based solution for the rail industry. As a Data Scientist (with focus on Statistics/Engineering) at KONUX, you seek mathematical robustness in solutions, love to code and get your hands dirty with large volumes of complex raw data from which you will seek to derive meaningful and actionable insights. Ideal candidates are self-motivated, self-directed and are happy to learn and adapt quickly. They will also be able to explore a wide range of statistical models and modeling techniques to understand and effectively break-down hard problems. KONUX scientists and engineers appreciate the difference between explaining and fitting statistical models, the importance of good metrics, and the tradeoff between exploration and exploitation.

The following is an opinion piece written by Ian Jefferies, president and CEO of the Association of American Railroads. Opinions are the author's own. The White House recently issued draft principles for governing the use of artificial intelligence across sectors, including transportation. While a recent report noted the guidance may be too vague to produce substantive benefits, the larger point is clear. Various forms of AI are here to stay and will only become more ubiquitous.

Toshiba Digital and Consulting Corporation and Mitsui have tested a digital twin software on the route between London's Stansted Airport to London Liverpool Street station. It was in 2002, while at the University of Michigan, that Dr Michael Grieves wrote about bridging the gap between the virtual and real worlds using digital replicas of physical assets, processes or systems. Two decades on, his concept of a'digital twin' has the potential to revolutionise industry. Digital twins use sensors to gather data in real time, which is then processed in a cloud-based system before being compared with other business and contextual data. The resulting analysis enables the operator to predict problems, optimise critical processes, and drive innovation and performance.

Most companies which responded to us were relatively young (2-5 years old). They were all already engaged in at least one other industry, most commonly manufacturing, but automotive and aerospace were also popular responses. When asked what they could offer the rail industry, suppliers cited many of the applications of this technology we have already described in this series, such as asset management, improving safety, and improving the customer experience. One company reported they were developing scalable mobile data solutions to match rural and city performance which would improve coverage across the rail network. Other interesting applications in development included intrusion detection devices and behavioural analytics.

The 2018 University of Delaware Department of Civil and Environmental Engineering "Big Data in Railroad Maintenance Planning Conference," held Dec. 13-14, spotlighted the progression the industry is making in dealing with Big Data--"converting the mountain of data collected by railway systems into effective maintenance planning information, with a focus on railway needs and practical applications. More than 200 students, consultants, suppliers and railroaders from both the freight and passenger sectors attended the fifth annual conference, organized by Dr. Allan M. Zarembski, Professor and Director of the university's Railroad Research and Safety Program. Federal Railroad Administrator Ron Batory was a featured speaker, along with presentations from such companies as Norfolk Southern, BNSF, CN, ENSCO, GREX, RailInc, Alstom, Uptake and VisioStack. The February 2019 issues of Railway Age and Railway Track & Structures will contain feature articles on the annual event. Following is a summary, as presented by David Staplin, consultant to HNTB, Amtrak Deputy Chief Engineer (retired) and University of Delaware Railroad Advisory Board Chairman, with additional reporting by railroad economist and Railway Age Contributing Editor Jim Blaze. "In prior years, the presentations focused on dealing with data, on potential," said Staplin. "This year, we are seeing more problems tackled and solved.

We work with vast quantities of travel data (127,000 journeys are taken by our customers every day) to enhance the travel experience with new, smart innovations. These innovations are designed to help customers in many ways, from saving them money to helping them find the best ticket for their journey in a quick and easy way. One of the projects my team has been working on recently is enhanced disruption notifications for our customers. Soon to launch in beta in our voice app, enhanced disruption notifications use machine learning and natural language processing to'read' the rail operators' Twitter feeds, analyse them and share relevant contextual journey updates matched to the individual traveller. It's a first for the rail industry in the UK and we're really excited to launch it.

Featherston spoke with SearchCIO at the recent Cloud Expo in New York. In this video, he explains how machine learning systems can help CIOs identify patterns in big data sets and delineates how the rail industry is saving money on maintenance costs by putting machine learning algorithms to use. He also explains how to identify what projects are right for machine learning enhancements and enumerates some of the challenges that CIOs should expect when implementing machine learning for big data analytics. What opportunities do machine learning systems offer CIOs? Ed Featherston: It's an interesting new world for CIOs because they've always had lots of data, have tried to analyze it in different ways and identify the patterns that are in there.

Within the rail industry, anything which helps keep trains moving, avoiding operational delays and improves customer experience, is worth pursuing. Many OEMs are now investing significant resources into one of the most valuable and potentially rewarding currencies in business: Big Data. In rail, and specifically when it comes to rolling stock maintenance, big data is synonymous with Condition Based Maintenance (CBM) and Predictive Maintenance (PM). Thanks to the rapidly expanding scale of manufacturing and asset maintenance industries, they are now adapting to the wider applications of advanced algorithms on consumer generated big data. Though CBM and PM are commonly adopted practices in rail industry, the scope of CBM is far wider than that of PM.