An Automated Method for Extracting and Analyzing Railway Infrastructure Cost Data

Abstract

The capability of extracting information and analyzing it so that it is in a common format is essential for performing predictions, comparing projects through cost benchmarking, and having a deeper understanding of the project costs. However, the lack of standardization and the manual inclusion of data make this process very time-consuming, unreliable, and inefficient. To tackle this problem, a novel approach with a big impact is presented combining the benefits of data mining, statistics, and machine learning to extract and analyze the information related to railway infrastructure cost data. To validate the suggested approach, data from 23 real historical projects from the client network rail were extracted, allowing their costs to be comparable. Finally, some machine learning and data analytics methods were implemented to identify the most relevant factors allowing cost benchmarking to be performed. The presented method proves the benefits of data extraction for gathering, analyzing, and benchmarking each project in an efficient manner, and to develop a deeper understanding of the relationships and the relevant factors that matter in infrastructure costs.