Thibaut Jean Pierre Dubernet: Katalogdaten im Herbstsemester 2018
|Name||Herr Dr. Thibaut Jean Pierre Dubernet|
|Departement||Bau, Umwelt und Geomatik|
|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.
Horni, A., K. Nagel and K.W. Axhausen (eds.) (2016) The Multi-Agent Transport Simulation MATSim, Ubiquity, London
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.|