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.

None.

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

The student

• has developed advanced abilities in visual- and material articulation and aesthetic application.
• has gained advanced knowledge of material engagement and how the world of things, artifacts, and material signs can be embodied through practical exploration.
• has gained advanced knowledge about interplay between material-aesthetics and object­-semantics.
• has gained knowledge about aesthetic strategies, methods and theory within a practical design research project.

Skills

The student is capable of:

• independently running a discovery-led process and stating a relevant research question.
• developing and recognizing relevant practical working methods.
• developing concept- and material model studies within a specific context.
• analyzing and reflecting on form and material application using relevant terminology.
• dissecting and refining design models systematically.
• analyzing and synthesizing experience from observations and various perceptions.
• exhibiting and presenting their research using relevant terminology and tools.

General competence

The student:

• is familiar with the theoretical basis of a practice based-research.
• understands the need for and use of practical exploration.
• is familiar with the terminology and language of practice-based research

The most important teaching and learning methods for this course are discussions, group work, lectures, studio courses and tutoring.

Individual or group portfolio examination. The portfolio consists of:

• Design-process documentation including end-product (model studies and iterations responding to the problem definition or research area), timeline with outline of process, documentation of model-studies and presentation of end-product.
• End-reflection in appropriate media (written, video, poster etc.) on the design-process and end-product.

The examination result can be appealed.

Grade scale A-F.

Two internal. External examiner is used periodically.

• Material properties in a specific context.
• Design methodology.
• Product communication and semantics.
• Aesthetic principles of form and function in a specific context.
• Experimental material practice.

Graded scale A-F.

1) Two internal examiners

2) Two internal examiners

External examiners are used regularly.

Chaoru Lu: chaorulu@oslomet.no