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AH2170 Transport Data collection and Analysis 7.5 credits

Evaluation, design, planning and operation of transportation system requires rich data, and systematic and efficient data collection plans.

The need for data and techniques for data analysis must  be adapted to the problem and site in question. Different types of data require different reduction techniques as well as methods for accurate statistical data analysis.

The course will provide knowledge on data collection and analysis methods as well as selection and interpretation of appropriate statistical tests that are relevant to the solution of the studied problem.

About course offering

For course offering

Autumn 2024 Start 26 Aug 2024 programme students

Target group

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Part of programme

Master's Programme, Energy Innovation, åk 2, SMCS, Recommended

Master's Programme, Transport and Geoinformation Technology, åk 1, Conditionally Elective

Master's Programme, Vehicle Engineering, åk 1, Conditionally Elective

Master's Programme, Vehicle Engineering, åk 2, Conditionally Elective


P1 (7.5 hp)


26 Aug 2024
27 Oct 2024

Pace of study


Form of study

Normal Daytime

Language of instruction


Course location

KTH Campus

Number of places

Places are not limited

Planned modular schedule


For course offering

Autumn 2024 Start 26 Aug 2024 programme students

Application code



For course offering

Autumn 2024 Start 26 Aug 2024 programme students


Fariya Sharmeen (


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Course coordinator

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Headings with content from the Course syllabus AH2170 (Autumn 2020–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

  • Transportation and geoinformation data needs
  • Sampling and sample statistics.
  • Descriptive statistics and outliers
  • Hypothesis testing and confidence Intervals
  • Linear regression and applications (in transport and traffic)
  • Maximum estimation likelihood method and applications
  • Other data analysis and model building methods

The content of the course is presented and trained in tutorials. Applications are in traffic studies, transport planning, safety studies and spatial analysis. Further training in field surveys and data analysis, model building and interpretation is carried out in the form of comprehensive project work.

The project covers all the major steps that have to be undertaken including report preparation, discussion of the results. The students will also present their results for discussion.

Intended learning outcomes

  • Identify appropriate methods for transportation, traffic and spatial data collection.
  • Understand transportation and geoinformation data needs
  • Understand the role sampling the data collection
  • Use descriptive statistics for the analysis and preparation of data
  • Perform outlier analysis
  • Perform statistical inference for hypothesis testing and interval estimations
  • Specify and estimate linear regression models and discrete choice models
  • Apply methods and interpret results using statistical software
  • Design and perform stated-preference study
  • Discuss and compare linear regression models and discrete choice models and their attributes

Literature and preparations

Specific prerequisites

Bachelor’s degree in engineering, science, economics, planning or a similar degree, with at least 60 cr (ECTS) in mathematics, physics, statistics and/or computer science, as defined in the admission requirements for the Master’s programme in Transport and Geoinformation Technology. Together with documented proficiency in English corresponding to English B.

Recommended prerequisites

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Possible literature could include:

S. Washington, M. Karlaftis, F. Mannering, Statistical and Econometric Methods for Transportation Data Analysis (2003).

Class handouts and material on Bilda.

Other useful books:

  • M. Ben-Akiva, S. Lerman, Discrete Choice Analysis: Theory and Application to Travel Demand, MIT Press, 1987.
  • J. de D. Ortúzar and L.G. Willumsen, Modelling Transport (2002).
  • O’Flaherty (ed.), Transport Planning and Traffic Engineering, chapter 12-13, 1997.

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

A, B, C, D, E, FX, F


  • PROA - Project assignment, 3.5 credits, grading scale: P, F
  • TENA - Written Examination, 4.0 credits, grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Other requirements for final grade

A mandatory written examination equivalent to 4.0 cr with grading scale A-F and a mandatory project assignment equivalent to 3.5 cr with grading scale P/F. The course will have grading scale A-F, where the course grade will be determined by the grade on the written examination and the project work.

Opportunity to complete the requirements via supplementary examination

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Opportunity to raise an approved grade via renewed examination

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Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

Main field of study

Built Environment

Education cycle

Second cycle

Add-on studies

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Fariya Sharmeen (