Principal Investigator

Nii Attoh-Okine, University of Delaware

Project Description

The project looks at various track geometry field data and performs in-depth exploratory data analysis investigating the statistical distributions and variability of the various track geometry data variables. Correlation of various track geometry parameters will be investigated by employing various linear and rank correlation dependency measures. The project also attempts to analyze and present the track geometry data using multiway data analysis (also known as tensor factorization) which will address the multidimensional nature of the dataset.

Implementation of Research Outcomes

TBD (Ongoing)

Final Report

Exploratory Data Analysis of Track Geometry Data

Outputs:

1 Thesis

• Mohammed Ahmed, Predicting Track Geometry Using Machine-Learning Methods, Thesis in Civil Engineering at the University of Delaware, 2023