• Transport geographical information needs
•  Sampling and statistical tests
• Hypothesis test and confidence interval
• Linear regression and applications (in transport and traffic)

After passing the course, the student should be able to

• Identify appropriate methods for transport traffic and spatial data collection
• Understand transport data needs
• Understand the role of the sample in the data collection process
• Be able to draw statistical conclusions from hypothesis test and interval estimations
•  State and estimate linear regression models and discrete choices models
• Apply methods and interpret results by means of statistical software

Published in Canvas course page.

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Python

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• LABA - Hand in assignment, 2.0 credits, grading scale: P, F
• TENA - Written exam, 2.5 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.

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

Examiner decides, based on recommendation from KTH Royal Institute of Technology's coordinator for disabilities about possible adapted examination for students with documented, permanent disabilities. The examiner may permit other examination formats at the re-examination of individual students.

LABA - Hand in assignment, 2.0 credits

Divided into 2 assignments with grading scale P/F. 

TENA - Written exam, 2.5 credits

Exam in computer rooms with Safe Exam Browser (SEB).

Final grade comes to establish of grade on examination and labs with grading scale A-F.

Exam:

Fx grade holders have the opportunity to submit an extra assignment to Pass (grade E) or take re-exam to obtain higher grades but not both.

Lab assignments:

If a student fails in any or both the lab assignments they will receive a deadline to re-submit before the re-exam.  

There is no opportunity to raise an approved grade via renewed examination.

Within 21 working days after the exam through Canvas course page.

• 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.

Students can work in groups and seek peer support for coding. However the lab assignment reports should be completed individually. Plagiarism is strictly prohibited. If AI is used in any part of the assignments that should de disclosed clearly and honestly. Failure to do so can result in a F grade.

Course syllabus for AG2301 valid from Autumn 2022