Thibaut Jean Pierre Dubernet: Katalogdaten im Herbstsemester 2018 |
Name | Herr Dr. Thibaut Jean Pierre Dubernet |
Departement | Bau, Umwelt und Geomatik |
Beziehung | Dozent |
Nummer | Titel | ECTS | Umfang | Dozierende | |
---|---|---|---|---|---|
101-0491-00L | Agent Based Modeling in Transportation | 6 KP | 4G | T. J. P. Dubernet, M. Balac | |
Kurzbeschreibung | This lectures provides a round tour of agent based models for transportation policy analysis. First, it introduces statistical methods to combine heterogeneous data sources in a usable representation of the population. Then, agent based models are described in details, and applied in a case study. | ||||
Lernziel | At the end of the course, the students should: - be aware of the various data sources available for mobility behavior analysis - be able to combine those data sources in a coherent representation of the transportation demand - understand what agent based models are, when they are useful, and when they are not - have working knowledge of the MATSim software, and be able to independently evaluate a transportation problem using it | ||||
Inhalt | This lecture provides a complete introduction to agent based models for transportation policy analysis. Two important topics are covered: 1) Combination of heterogeneous data sources to produce a representation of the transport system At the center of agent based models and other transport analyses is the synthetic population, a statistically realistic representation of the population and their transport needs. This part will present the most common types of data sources and statistical methods to generate such a population. 2) Use of Agent-Based methods to evaluate transport policies The second part will introduce the agent based paradigm in details, including tradeoffs compared to state-of-practice methods. An important part of the grade will come from a policy analysis to carry with the MATSim open-source software, which is developed at ETH Zurich and TU Berlin and gets used more and more by practitioners, notably the Swiss rail operator SBB. | ||||
Literatur | Agent-based modeling in general 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, mostly scientific articles, will be recommended throughout the course. | ||||
Voraussetzungen / Besonderes | There are no strict preconditions in terms of which lectures the students should have previously attended. However, knowledge of basic statistical theory is expected, and experience with high-level programming languages (Java, R, Python...) is useful. |