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Intelligent Transport Systems

Course Code:

GEO9150

Semester:

9th Semester

Specialization Category:

S.

Course Hours:

4

ECTS:

5

Eclass Link:

https://eclass.uniwa.gr/courses/GEO233/


LEARNING OUTCOMES

After the successful completions of the course, students will be able to:

  • Learn the characteristics of autonomous vehicles and driving assistant systems
  • Realize the benefits of applying artificial intelligence to transport
  • Understand real-time management systems and telematics
  • Solve simple applications in a traffic simulation program

 

General Competences

After the successful completions of the course, students acquire the following knowledge and skills:

  • Search for analysis and synthesis of data and information, with the use of the necessary technology
  • Adaptation to new situations
  • Autonomous work
  • Production of free, creative and inductive thinking
  • Exercise criticism and self-criticism
  • Work in an interdisciplinary environment

 

SYLLABUS

Theoretical part of the Course

  1. Introduction to the application of artificial intelligence in transport.
  2. Autonomous vehicles
  3. Driving assistant systems
  4. Telematics in transport
  5. Network optimization
  6. Real-time management systems
  7. Centralized and distributed controls and decision-making methods
  8. Applied statistical standardization
  9. Stated Preference Surveys
  10. Traffic simulation software

 

Lab Part of the Course

Preparation of a group theme (groups of 4 people) based on a questionnaire survey with the method of stated preference analysis.

STUDENT PERFORMANCE EVALUATION

Language of evaluation: Greek
Theoretical part of the Course
• Written exam (70%)
Lab Part of the Course
• Partial and Overall Presentation of a semester topic (30%)

 

ATTACHED BIBLIOGRAPHY

1. Autonomous Driving, Technical, Legal and Social Aspects, by Markus Maurer, Christian Gerdes, Barbara Lenz, Hermann Winner, SpringerLInk edition
2. Marsland, S. (2014). Machine learning: an algorithmic perspective. CRC press.
3. Kruse, R., Borgelt, C., Klawonn, F., Moewes, C., Steinbrecher, M., & Held, P. (2013). Computational intelligence: a methodological introduction. Springer Science & Business Media.
4. Karlaftis, M. G. and Vlahogianni, E. I. (2011). Statistics versus Neural Networks in Transportation Research: Differences, Similarities and Some Insights, Transportation Research Part C: Emerging Technologies, 19(3), 387-399.
5. Engelbrecht, A. P. (2007). Computational intelligence: an introduction. John Wiley & Sons.
6. TRB (2007). Artificial Intelligence in Transportation: Information for Application, Transportation Research Circular E-C113, Transportation Research Board, Washington DC.
7. Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.