Intelligent Solutions in Transportation (4200)

  
Coordinating lecturer :Prof. dr. ir. Ansar YASAR 
  
Member of the teaching team :dr. Dimitrios ZAVANTIS 
 De heer Satria Bagus WICAKSONO 
 dr. ir. Youssef EL HANSALI 


Language of instruction : English


Credits: 4,0
  
Period: quarter 1 (4sp)
  
2nd Chance Exam1: Yes
  
Final grade2: Numerical
 
Exam contract: not possible


 
Sequentiality
 
   No sequentiality

Prerequisites

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.



Content

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 some practical systems will be explained during the self-learning tasks. Will be discussed further 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)


Organisational and teaching methods
Organisational methods  
Lecture  
Project  
Response lecture  
Small group session  
Teaching methods  
Demonstration  
Discussion/debate  
Exercises  


Evaluation

Quarter 1 (4,00sp)

Evaluation method
Oral evaluation during teaching period20 %
Presentation
Evaluation method in consultation with student during teaching period10 %
Transfer of partial marks within the academic year
Conditions transfer of partial marks within the academic yearThese scores will be kept the same for the 2nd chance.
Explanation (English)Class Interaction & Peer Feedback
Other evaluation method during teaching period10 %
Other Participation in the self-learning session/task
Transfer of partial marks within the academic year
Conditions transfer of partial marks within the academic yearThese scores will be kept the same for the 2nd chance.
Written exam60 %
Closed-book

Second examination period

Evaluation second examination opportunity different from first examination opprt
No
Explanation (English)New Written (open-book) exam. Other scores will be kept from the 1st
chance.
 

Recommended reading
  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://ebookc entral.proquest.com/lib/ubhasselt/detail.action?docID=1214 471&pq-origsite=summon
 

Recommended course material
 
  • No handbook required.
  • The course will be composed on lecture notes & slides from the professors/lecturers.
  • For the self-learning part you will be provided with some guided material.
  • There could be a number of guest lectures invited to this course with certain domain expertise.


Learning outcomes
Master of Transportation Sciences
  •  EC 
  • EC1: The holder of the degree applies knowledge in an independent and self-directed manner. He/she is able to critically plan, guard, manage and evaluate his/her own learning processes and to take care of his/her own (quality) control.

     
  •  DC 
  • DC3: The student has an advanced level of knowledge and insight, characteristic of scientific work in the field of transportation sciences.

     
  •  DC 
  • DC5: The student works in an autonomous and self-managing way, engaging in self-reflection and striving for continuous improvement.

  •  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.

     
  •  DC 
  • DC1: The student can evaluate how policy framework(s) are established and can propose alternatives.

     
  •  DC 
  • DC2: The student investigates which parts of an (inter)national policy framework are applicable to various regions and determines the critical success factors and the system in which it should function.

  •  EC 
  • EC7: The holder of the degree is able to function as a member of a (multidisciplinary) team and has a good assessment of his/her own role within the team/organization and in the broader social and international context.

     
  •  DC 
  • DC1: The student gets a better view of his/her role as a transportation scientist in the broad social and/or international context.

     
  •  DC 
  • DC5: The student works towards sustainable solutions in consultation with others.

 

  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Offered inTolerance3
1st year Master of Transportation Sciences (incl ICP) J
Exchange Programme Transportation Sciences J



1   Education, Examination and Legal Position Regulations art.12.2, section 2.
2   Education, Examination and Legal Position Regulations art.15.1, section 3.
3   Education, Examination and Legal Position Regulations art.16.9, section 2.