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Course presentation
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.
Headings denoted with an asterisk ( * ) is retrieved from the course syllabus version Autumn 2020
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
Preparations before course start
Literature
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.
Support for students with disabilities
Students at KTH with a permanent disability can get support during studies from Funka:
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.
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.
