SMUA4100 Transport Policy and Transport Management Course description

This module introduces students to key concepts in transport management and policy making and provides them with the background knowledge necessary to understand transport system development. The module starts with considering the evolution of transport, and transport management strategies; car-oriented, public-transport-oriented and a demand-oriented approach (mobility management and Mobility as a Service). Introduction to management process of policy making and who is responsible for transport policy e.g., supranational, central and local government structures and the role of the private sector. Problems and trends in transport demands and an introduction to policy perspectives.

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

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:

• the historical development of transport systems and urbanity
• the transport planning and policy-making process
• regulation, ownership and funding structures for transport systems
• current guidance on transport and land use planning

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Skills:

The student is capable of:

• understanding and explaining why transport planning is a necessary aspect of governance
• describing the transport policy process and key issues determining policy options
• interpret different policy options and their implications
• critically analyzing the development of transport systems

General competence:

The student is capable of:

• identifying the outcomes of transport management and policy interventions in the field
• reviewing a range of literature and using scholarly articles to keep up with latest developments in the field
• working as part of a team to solve a problem
• presenting results in a scholarly, professional manner through written reports and oral presentations.

This course;will consist of lectures, seminars (including invited lecturers, discussions and presentations) and field studies to provide theoretical;content and preliminary hands-on experience.;

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:

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, R, or Weka)
• using the modelling methods to support intelligent transport system management and policy development

General competence:;

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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.;;