101-0478-00L  Survey Methods and Discrete Choice Analysis

SemesterSpring Semester 2024
LecturersB. Schmid
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
101-0478-00 GSurvey Methods and Discrete Choice Analysis4 hrs
Wed09:45-11:30HIL F 36.1 »
Thu08:00-09:35HIL F 36.1 »
B. Schmid

Catalogue data

AbstractComprehensive introduction to survey methods in transport planning and modeling of travel behavior, using advanced discrete choice models.
Learning objectiveEnabling the student to understand and apply the various measurement approaches and models of travel behaviour research.
ContentBehavioral model and measurement; travel diary, design process, hypothetical markets, parameter estimation, econometrics, pattern of travel behaviour, market segments, simulation, advanced discrete choice models
Lecture notesVarious papers and notes are distributed during the course.
LiteratureThe course heavily builds on Train, K. E. (2009) Discrete Choice Methods with Simulation, Cambridge University Press.
Prerequisites / NoticeThis introduction in survey methods and (advanced) discrete choice modelling requires basic programming knowledge in the statistical software R. Solid understanding of statistical modeling and econometrics is of advantage.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Decision-makingfostered
Media and Digital Technologiesfostered
Problem-solvingassessed
Project Managementfostered
Social CompetenciesCommunicationfostered
Cooperation and Teamworkfostered
Personal CompetenciesAdaptability and Flexibilityfostered
Creative Thinkingfostered
Critical Thinkingassessed
Integrity and Work Ethicsfostered
Self-direction and Self-management fostered

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits6 credits
ExaminersB. Schmid
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.

Learning materials

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Only public learning materials are listed.

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Offered in

ProgrammeSectionType
Data Science MasterInterdisciplinary ElectivesWInformation
Geomatics MasterRecommended Electives of Master Degree ProgrammeWInformation
Mathematics MasterTransportation ScienceWInformation
Spatial Development and Infrastructure Systems MasterMajor in Transport Systems and BehaviourWInformation
Spatial Development and Infrastructure Systems MasterMajor in Transport Systems and BehaviourWInformation
Science, Technology, and Policy MasterUrbanization and PlanningWInformation
Science, Technology, and Policy MasterCase StudiesWInformation