Single-Course English 5 ECTS

Data analytics in transport with emphasis on railways

Overall Course Objectives

The course is focused on linear regression analysis, which is an important tool in marketing, planning, operations and causal analysis. The course functions as a further study after 02403, with coverage of multiple linear regress and stochastic regression (multinomial and Poisson). Students practice with a variety of data types and problem descriptions, and multiple examples from railways.

The course uses “R” statistics software.

Learning Objectives

  • Formally define problems and identify the relevant analysis
  • Choose, collect, format, and clean data according to recommended standards
  • Describe data by means of descriptive statistics, ANOVA, and test for significance
  • Explain the theoretical basis for linear regression and “ordinary least squares”
  • Perform residual analysis, apply data transformations, and evaluate model fit
  • Fit multiple regression models with stepwise regression
  • Formulate decision models with logistic regression
  • Formulate demand and failure models with Poisson regression
  • Describe operations problems in railways and applications of data analysis in planning and forecasting
  • Evaluate case studies in forecasting or causal analysis
  • Develop skills in relevant statistical analysis software, especially “R”

Course Content

The course introduces linear regression and then continues to advanced methods. Learning objectives cover both methods and problem solving tips. A variety of data sets from mobility and transport are explored to demonstrate complex models, but also to demonstrate what can go wrong, and how to avoid false interpretations. The course ends with an emphasis on real world data and the possibility of multiple interpretations of that data.

The course meets together with diplom course 62637, but the bachelor students receive different exercises and evaluation.

Recommended prerequisites

62668, Students should have previously passed a course covering descriptive statistics, distribution fitting, and hypothesis testing. This is a firm requirement.

Teaching Method

Lectures, exercises, some group work and final exam.



Research group: Production, Transportation and Planning
Elective: Decision Support in Engineering: 3/5. semester

See course in the course database.





13 weeks


Engineering Technology


DTU Ballerup Campus

Course code 63880
Course type Bachelor
Semester start Week 35
Semester end Week 48
Days Tues 13-17

7.500,00 DKK