Language of instruction : English |
Sequentiality
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No sequentiality
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| Degree programme | | Study hours | Credits | P1 SBU | P1 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
| part 1 Master of Transportation Sciences (by distance learning) | Compulsory | 108 | 4,0 | 108 | 4,0 | Yes | Yes | Numerical | |
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| Learning outcomes |
- EC
| EC2: The holder of the degree has in-depth knowledge and understanding of the concepts, methods, and (research) techniques of transportation sciences. He/she is able to apply the concepts, methods and (research) techniques in the field of transportation sciences adequately and autonomously. | - EC
| EC4: The holder of the degree considers the society as a whole as an important stakeholder and reflects on the social relevance and consequences of recommendations/solutions and projects/assignments in a critical manner. In doing so, the holder of the degree strives, among other things, to have a sustainable impact on the region. | - EC
| EC5: The holder of the degree is made aware of and has insight into the regional and international policy framework, similarities and differences with respect to transport policies. The holder of the degree is encouraged to get in touch with various (inter)national stakeholders active in the field of transportation. |
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| EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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The students should have a basic knowledge of traffic science terminologies. The students should have good analytical skills and critical thinking to interpret various transportation-related issues. It is also advisable to have some basic IT knowledge.
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Description:
This courses enables transportation sciences students from various backgrounds to intelligent and smart transportation solutions that influence technological advancements. Traditional methods such as advanced traveler information and dynamic message signs are now part of a broader landscape that features deployments of connected, automated, and autonomous vehicle technologies with infinite possibilities. The effective utilization of current emerging technologies requires an interdisciplinary approach with focus on the benefits, operating characteristics, deployment considerations, and potential shortcomings of the deployed solutions in various transport scenarios. Some of the key topics will focus on hands-on case studies that encourage critical thinking to assess opportunities for the continuous improvement of deployed and new solutions, tailored to the local context.
Objectives:
The end-competences of this course include (but not limited to):
- A conceptual understanding of the most common intelligent solutions in transportation with their anticipated benefits, and their shortcomings.
- A basic understanding of how emerging technological developments would transform the transportation system in the near future.
- Knowledge to plan for the practical deployment of such solutions.
- Insights into the vast potential of possible approaches to apply current technologies in ways that would facilitate useful interactions between transportation supply and demand, performance measures, planning, and policy making.
- Experience with SUMO (Simulation of Urban Mobility) during the self-learning sessions/task. More details will be given in the class.
Content:
- Traveler information and advisory systems (such as dynamic signage, onboard equipment, CCTV, smartphone applications)
- Traffic flow control (including adaptive signaling, ramp metering, variable speed limits/warnings, transit priority
- Automatic electronic payments (such as toll tags, transit cards, RFID, smartphones)
- Pre-clearance systems (including vehicle classification, high-speed weigh-in-motion, roadside inspection, freight scanners)
- Technologies for managed facilities (such as HOV and other dedicated lanes)
- Smart parking solutions (occupancy sensors, parking reservation systems, metering technologies, etc.)
- Security and privacy impacts of technology deployments
- Connected vehicles technologies and architectures
- Major applications of connected vehicle technologies (information, ride sharing, crash avoidance, driver assist, platoons, etc.)
- Automated and autonomous vehicles
- Cloud computing (advisory systems: security threats, weather, work zones, incidents, detours, traffic)
- Big Data (travel time forecasting, maintenance decision-support, flow optimization, parking availability)
- Remote sensing (surveillance, satellites, UAV technology, emergency response incident management)
- Smart solutions in transportation for improving environment
- Sustainable mobility solutions in a cross-sectoral context (e.g. tourism)
The majority of your learning will be done through self-study, based on the course material that is made available online. You will have access to a range of useful online learning materials such as online lectures, reading materials, as well as access to thousands of e-books, online journals and other resources via our online university library. Approximately mid-semester, an online interactive Question and Answer session will be organised, where students can meet the course lecturer and fellow students online and have the opportunity to go deeper into the course material.
All evaluations will be done online, whether by submission of assignments or by taking written or oral exams online, or a combination of assignments and exams. Written exams will be done with online proctoring (exam supervision) to retain the integrity of a supervised exam, while providing the flexibility of an online platform. For the online exams, distance learning students should ensure to have a laptop/pc with a working webcam and microphone, a charged mobile device (phone/tablet) and a stable internet connection (minimum upload speed of 1.5 MB/second), as well as a quiet, secluded room to take the exam in.
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Lecture ✔
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Project ✔
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Response lecture ✔
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Small group/individual work sessions ✔
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Demonstration ✔
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Discussion/debate ✔
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Exercises ✔
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Presentation ✔
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Period 1 Credits 4,00
Evaluation method | |
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Written evaluaton during teaching periode | 25 % |
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Transfer of partial marks within the academic year | ✔ |
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Oral evaluation during teaching period | 25 % |
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Transfer of partial marks within the academic year | ✔ |
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Additional information | The students can work on their assignments either in groups or individually. |
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Second examination period
Evaluation second examination opportunity different from first examination opprt | |
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Explanation (English) | Only the written (closed-book) exam will be repeated. All other scores will be kept from the first chance exam. |
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Recommended reading |
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Intelligent Transport Systems in Europe: Opportunities for Future Research,Mike Mcdonald; Hartmut Keller; Job Klijnhout; Vito Mauro; Richard Hall; Angela Spence; Christoph Hecht; Oliver Fakler,World Scientific Publishing Company,Available as e-book: https://ebookcentral.proquest.com/lib/ubhasselt/detail.action?docID=1214471&pq-origsite=summon |
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Recommended course material |
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- No handbook required.
- The course will be composed on lecture notes & slides from the professors/lecturers.
- For the small (self-learning) practical part, a SUMO practical guide book (pdf) will be available online.
- There could be a number of guest lectures invited to this course with certain domain expertise.
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1 Education, Examination and Legal Position Regulations art.12.2, section 2. |
2 Education, Examination and Legal Position Regulations art.16.9, section 2. |
3 Education, Examination and Legal Position Regulations art.15.1, section 3.
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Legend |
SBU : course load | SP : ECTS | N : Dutch | E : English |
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