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 master's thesis consists of a:

(1) A dissertation of a scientific nature (e.g. monograph, research paper (review papers are not accepted), etc.). It must not exceed 100 pages, excluding appendices (master thesis template provided on Canvas).

Or

(2) A project work (e.g. startup, software, prototype, etc.), original and specially created for this purpose. A written report, not exceeding 40 pages excluding appendices, should be submitted along with the project work. (master thesis's template provided on Canvas)

and

(3) An oral presentation (15-20min) and examination (20-30min). The student will be informed about the time and place of the presentation on Canvas.

The dissertation/report should be written and presented in English. It is necessary to provide an abstract in both English and Norwegian.

Only one grade will be awarded for the dissertation/project work and the oral examination, as they are assessed together. No separate grades will be given for each component. Grades will be published within six weeks after the submission deadline. Students have the right to request an explanation of their grade.

Appeal: If the student appeal the grade, the oral presentation and examination should be retaken.

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.

The master's thesis is an independent scientific or industrial project carried out by a student or a group of students (max 2), with a minimal guidance of a faculty member in one of the key subject areas of the Master program in Civil Engineering (MABY) relevant to one of the chosen specialisations: Structural Engineering, Building Technology, Transport Infrastructure Engineering, Geotechnical Engineering and Smart Water Process and Infrastructure Engineering. MABY5900 builds upon the foundational work of MABY5000, and the thesis topic and supervisor are typically chosen during MABY5000, although changes can be made later. Should a change of supervisor be required, the course coordinator for the master's thesis is responsible for assisting the student in identifying and securing a new supervisor. Students who do not take MABY5000, such as those in the Smart Water Process and Infrastructure Engineering specialization, are required to select their thesis topics according to the guidelines specific to their specialization.

The master's thesis can be related to relevant issues within the respective industries or ongoing research projects at the department. Group work (max 2) is encouraged if the master's thesis is related to a large-scale industrial or research project. The master's thesis should adopt a scientific approach, and its results should contribute to the existing body of knowledge or explore innovative methods or topics. Guidelines for master's theses at the Faculty can be found here: Guidelines for master's theses at the Faculty of Technology, Art and Design - Student - minside (oslomet.no)

The master's thesis builds on all the courses taught in the previous semesters. Students must have passed all their first-year exams before they are entitled to supervision on the work on their master's thesis.

After completing the master's thesis, the student is expected to gain several skills, defined in terms of knowledge, skills and general competence, that go beyond the realm of academic expertise and are highly transferable to various professional contexts.

Knowledge:

The student:

• has specialized knowledge in the topic of the master's thesis.
• has in-depth knowledge of theories and methods of relevance to the topic of the master's thesis.
• has knowledge of new solutions, technologies or processes in the field.

Skills:

The student:

• is capable of analyzing and taking a critical approach to different sources of information and using them to structure and formulate argumentation in the field.
• is capable of analyzing existing theories, methods and standardized solutions and of working independently on practical and theoretical problem-solving.
• is capable of using relevant methods for research and development work in an independent manner.
• is capable of carrying out an independent, delimited research or development project under supervision and in accordance with applicable research ethical standards.

General Competence :

The student:

• is capable of applying his/her knowledge and skills to new fields for the purpose of carrying out advanced tasks and projects.
• is capable of communicating extensive independent work and masters the forms of expression used in the field.
• is capable of communicating on issues, analyzes and solutions in the field of structural engineering and building technology, both with specialists and with the general public.
• is capable of contributing to new ideas and innovation processes.

The master's thesis is an independent piece of work planned and carried out by a student or a group of students (max 2). The student is responsible for conducting and managing his/her own master's thesis, including contact with the supervisor. The master's thesis shall take the form of a dissertation of a scientific nature (eg monograph, research paper (review papers are not accepted), etc.) or project work (eg startup, software, prototype, etc.), original and specially created for this purpose, and an oral presentation.

If the master's thesis is conducted in cooperation with an external partner, the student(s) shall be assigned an external supervisor in addition to their internal supervisor and shall sign an agreement with the internal supervisor (contract provided on canvas) and external partner (agreements provided here: Student agreements |. Rights and duties - Student - minside (oslomet.no) .

There is no teaching offered in this course.

None