101-0491-00L Agent Based Modeling in Transportation
| Semester | Autumn Semester 2023 |
| Lecturers | M. Balac |
| Periodicity | yearly recurring course |
| Language of instruction | English |
Courses
| Number | Title | Hours | Lecturers | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 101-0491-00 G | Agent Based Modeling in Transportation | 4 hrs |
| M. Balac |
Catalogue data
| Abstract | This course provides an introduction to agent-based modeling in transportation. The lectures and exercises offer an opportunity to learn about agent-based simulation models' current methodology, focusing on MATSim, how agent-based models are set up, and perform a practical case study by working in groups. | ||||||||||||||||||
| Learning objective | At the end of the course, the students should: - have an understanding of agent-based modeling - have an understanding of MATSim - have an understanding of the process needed to set up an agent-based study - have practical experience of using MATSim to perform transportation studies | ||||||||||||||||||
| Content | This course provides an introduction to agent-based models for transportation policy analysis. Four essential topics are covered: 1) Introduction of agent-based modeling and its comparison to the traditional state of practice modeling 2) Introduction of MATSim, an open-source agent-based model, developed at ETH Zurich and TU Berlin, and its various parts 3) Setting up an agent-based model simulation, where different statistical methods used in the process will be introduced and explained. Here the open-source eqasim framework used at ETH Zurich to set up agent-based models will be introduced 4) Conducting a transport policy study. The case study will be performed in groups and will include a paper-like report. During the course, outside lecturers will give several lectures on using MATSim in practice (i.e., SBB). | ||||||||||||||||||
| Literature | Agent-based modeling in general Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the national academy of sciences, 99(suppl 3), 7280-7287. Helbing, D (2012) Social Self-Organization, Understanding Complex Systems, Springer, Berlin. Heppenstall, A., A. T. Crooks, L. M. See and M. Batty (2012) Agent-Based Models of Geographical Systems, Springer, Dordrecht. MATSim Horni, A., K. Nagel and K.W. Axhausen (eds.) (2016) The Multi-Agent Transport Simulation MATSim, Ubiquity, London (http://www.matsim.org/the-book) Additional relevant readings, primarily scientific articles, will be recommended throughout the course. | ||||||||||||||||||
| Prerequisites / Notice | There are no strict preconditions regarding which lectures the students should have previously attended. However, experience with at least one high-level programming language (Java, R, Python, or other) is recommended. For those without object-oriented programming experience, a crash course 101-0491-10 Basics of Java and Best Practices for Scientific Computing before the start of HS is recommended. | ||||||||||||||||||
Competencies |
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Performance assessment
| Performance assessment information (valid until the course unit is held again) | |
Performance assessment as a semester course | |
| ECTS credits | 6 credits |
| Examiners | M. Balac |
| Type | graded semester performance |
| Language of examination | English |
| Repetition | Repetition possible without re-enrolling for the course unit. |
Learning materials
| No public learning materials available. | |
| Only public learning materials are listed. |
Groups
| No information on groups available. |
Restrictions
| There are no additional restrictions for the registration. |