101-0417-00L  Transport Planning Methods

SemesterAutumn Semester 2022
LecturersK. W. Axhausen
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
101-0417-00 GTransport Planning Methods
Am 28.09.22 findet die Lehrveranstaltung im HPL D 32 statt,
4 hrs
Mon09:45-11:30HIL C 10.2 »
Wed09:45-11:30HIT H 42 »
28.09.09:45-11:30HPL D 32 »
K. W. Axhausen

Catalogue data

AbstractThe course provides the necessary knowledge to develop models supporting and also evaluating the solution of given planning problems.
The course is composed of a lecture part, providing the theoretical knowledge, and an applied part in which students develop their own models in order to evaluate a transport project/ policy by means of cost-benefit analysis.
Learning objective- Knowledge and understanding of statistical methods and algorithms commonly used in transport planning
- Comprehend the reasoning and capabilities of transport models
- Ability to independently develop a transport model able to solve / answer planning problem
- Getting familiar with cost-benefit analysis as a decision-making supporting tool
ContentThe course provides the necessary knowledge to develop models supporting the solution of given planning problems and also introduces cost-benefit analysis as a decision-making tool. Examples of such planning problems are the estimation of traffic volumes, prediction of estimated utilization of new public transport lines, and evaluation of effects (e.g. change in emissions of a city) triggered by building new infrastructure and changes to operational regulations.

To cope with that, the problem is divided into sub-problems, which are solved using various statistical models (e.g. regression, discrete choice analysis) and algorithms (e.g. iterative proportional fitting, shortest path algorithms, method of successive averages).

The course is composed of a lecture part, providing the theoretical knowledge, and an applied part in which students develop their own models in order to evaluate a transport project/ policy by means of cost-benefit analysis. Interim lab session take place regularly to guide and support students with the applied part of the course.
Lecture notesMoodle platform (enrollment needed)
LiteratureWillumsen, P. and J. de D. Ortuzar (2003) Modelling Transport, Wiley, Chichester.

Cascetta, E. (2001) Transportation Systems Engineering: Theory and Methods, Kluwer Academic Publishers, Dordrecht.

Sheffi, Y. (1985) Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods, Prentice Hall, Englewood Cliffs.

Schnabel, W. and D. Lohse (1997) Verkehrsplanung, 2. edn., vol. 2 of Grundlagen der Strassenverkehrstechnik und der Verkehrsplanung, Verlag für Bauwesen, Berlin.

McCarthy, P.S. (2001) Transportation Economics: A case study approach, Blackwell, Oxford.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits6 credits
ExaminersK. W. Axhausen
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationoral 30 minutes
Additional information on mode of examinationCompulsory continuous performance assessment tasks consisting of 3-4 exercises and a presentation in order to evaluate a transport project. These count towards 30% of the final grade (oral session examination). Not handing in an exercise will mean a grade of 1.
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

No public learning materials available.
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

There are no additional restrictions for the registration.

Offered in

ProgrammeSectionType
Civil Engineering MasterMajor in Transport SystemsWInformation
Civil Engineering MasterDigitalisation Specific CoursesWInformation
Data Science MasterInterdisciplinary ElectivesWInformation
Geomatics MasterCore ElectivesWInformation
Geomatics MasterMajor in PlanningWInformation
Integrated Building Systems MasterSpecialised CoursesWInformation
Mathematics MasterTransportation ScienceWInformation
Spatial Development and Infrastructure Systems MasterMajor in Transport Systems and BehaviourWInformation
Science, Technology, and Policy MasterCase StudiesWInformation