Machine learning techniques are now very common in many spheres, and there is a growing popularity of these approaches in macroeconomic forecasting as well. Are these techniques really useful in the prediction of macroeconomic outcomes? Are they superior in performance compared to their traditional counterparts? We carry out a meta-analysis of the existing literature in order
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[1], the University of Delaware’s annual “Big Data in Railroad Maintenance Planning” conference provides a forum for railroad and data analytics professionals to come together with academia and discuss the latest applications and research in railway-related data science. Clearly evident from these annual conferences, there is growing use of data analytics, often referred to as “Big Data,” to address maintenance and safety issues in all aspects of railroading: Engineering (Track and Structures), Equipment (Rolling Stock) and Transportation (Operations). This article will continue that discussion of the December 2020 conference, focusing on the Track and Right-of-Way-related applications of data analytics to the issues of railroad safety and maintenance. Although held in a virtual format, the conference organized by Drs. Allan M. Zarembski, Nii Attoh-Okine and Joseph Palese was able to attract more than 270 attendees from the full spectrum of railroad Big Data-related activit