SMUA4400 Transport Data Analytics Course description

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; 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.

The following must have been approved in order for the student to take the examination:

• minimum 80% participation in practical exercises at health pedagogy
• approved research question for the home examination by a given deadline

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:

Knowledge:

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

Skills:

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)
• using the modelling methods to support intelligent transport system management and policy development

General competence:

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

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.

Two individual assignments must be approved. Students who fail to meet the coursework requirements can be given up to one re-submission opportunity.

1) Project report prepared in groups, (max size 6 students) approx. 15 - 25 pages (excl. appendices), weighted 70%.

2) Oral presentation in the same groups and examination of the project report, weighted 30%.

Grading is individual, which means that grades may differ within each group. Both oral and written examinations can be presented/written in teams. To ensure that individual grading should be possible, each group of students will provide a written and signed statement in where each individual's contribution is clearly stated and explained.

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

Language of instruction: Norwegian

This course highlights and problematises core concepts in health pedagogy and the relationship between the concepts. Health pedagogy methods are illuminated, and operationalized. Digital solutions used for health challenges are illuminated and problematized. Through training and reflection, the student will acquire the expertise necessary to plan, execute and evaluate health pedagogy measures provided to persons with health challenges and their next of kin, and incorporate user knowledge when teaching patients/users and next of kin.

Emphasis is placed on interprofessional cooperation and the organisation of health pedagogy measures, as well as the importance of being a change agent in different arenas related to health promotion.

The student must have been admitted to the Master’s Programme in Health Sciences.

After completing the course, the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The student:

• can critically assess concepts and methods in health pedagogy

Skills

The student:

• can plan, implement, critically assess and document a health pedagogy scheme for patients/users or next of kin
• can apply health pedagogy knowledge and skills in interprofessional collaboration and in cooperation with patients/users and next of kin, in a practical training situation or at their place of work
• can discuss the planning and implementation of different health pedagogy methods, if relevant in cooperation with patients/users and next of kin

General competence

The student:

• can critically analyse and incorporate user knowledge in patient and next of kin training individually and in groups

The course will use varied, student-active work methods. Work and teaching methods include lectures, practical exercises in health pedagogy methods individually and in groups, group work and self-study.