Studieinfo emne SMUA4400 2022 HØST
SMUA4400 Transport Modelling and Analytics Emneplan
- Engelsk emnenavn
- Transport Modelling and Analytics
Master's Degree Programme in Smart Mobility and Urban Analytics
- 10 stp.
With the development of sensing technologies, transport digitalization generates and provides numerous data from different resources. This course will introduce models and applications of transport systems analysis in the context of transport studies and gain deeper insight into how these models help with the decision‐making process. Topics to be covered include data preprocessing, travel studies and analysis of data; machine learning methods; statistic methods; transportation systems forecast and analyses. Moreover, the course will provide a brief introduction to future sensing technologies and deep learning methods. The methods cover by this course will closely link to real world transport problem, such as travel demand modelling, accessibility, last-mile problem and other related issues.
No formal requirements over and above the admission requirements.
After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills, and general competence:
Upon successful completion of the course, the student will achieve knowledge about:
terminology and models for transport studies
statistical and machine learning methods
advanced sensing technologies
future development in the transport data analytics
Upon successful completion of the course, the student is capable of:
understanding and applying the proper knowledge and method to collect, process, and analyze transport data
applying statistical and machine learning methods with a proper interpretation of the methods used in transport modelling
making use of approved terminology and standardization in the field of transport analytics
optimum use of data analysis software (Python, R, or Weka)
using the modelling methods to support intelligent transport system management and policy development
Upon successful completion of the course, the student:
has deep insight into the transport data collection and data analysis methods
is able to apply proper methods to solve practical problems in different real-world conditions
is able to understand and explain the results of transport models
is able to present academic results and evaluations, both to specialists and to the general public
Arbeids- og undervisningsformer
This course will consist of lectures, one seminar (with invited lecturers, discussions and presentations), and lab sessions to provide theoretical content and preliminary hands-on experience. The students will be involved in peer feedback and the students are given a project task to work in groups during the semester.
Arbeidskrav og obligatoriske aktiviteter
Two individual assignments must be approved. Students who fail to meet the coursework requirements can be given up to one re-submission opportunity.
Vurdering og eksamen
1) Project report prepared in groups, approx. 15 - 20 pages (excl. appendices), weighted 70%.
2) Oral presentation and examination of the project report, weighted 30%.
All assessment parts must be awarded a pass grade (E or better) to pass the course.
Assessment parts: 1) can be appealed, 2) cannot be appealed
Graded scale A-F.
1) One internal examiner
2) Two internal examiners
External examiners are used regularly.
Chaoru Lu: firstname.lastname@example.org