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Agent Based Modeling in Transportation
Last Updated: 2026-06-03 00:07:25
Abstract
In this course, the students take on the role of transport modeler and planner. Through hands-on simulation using MATSim, we explore agent-based modeling - a powerful way to simulate how people move and interact in urban environments. Working in teams, students build and test realistic transport models, experiment with policies, and evaluate their effects in virtual urban landscapes.
Objective
By the end of the course, students will be able to: Grasp the core concepts of agent-based modeling (ABM) and its relevance to real-world transport systems. Understand and use MATSim, a leading open-source platform for large-scale agent-based mobility simulation. Design and set up a complete ABM study—from data to decisions—for a city-scale transportation challenge. Work collaboratively on a simulation project to test, analyze, and present mobility policies (e.g., bike lanes, public transport subsidies, emissions zones). Develop critical thinking about the impacts of policy decisions on equity, sustainability, and livability in urban transport.
Content
This highly interactive course teaches you how to create a digital copy of the world around you—a virtual testbed. With a focus on transport, we will cover the following topics: General overview of digital twin technology and their applications in transport Introduction of agent-based/individual-based methodology that stands at the foundation of digital twin models Techniques for developing synthetic representations of people and vehicles Use of LLMs in interacting with the digital twins - The future or a nice gimmick? Project work where you will use the virtual testbed to answer relevant societal questions, developing team, management, technical, and critical thinking skills. During the course, you will also be able to hear and discuss with our industrial/start-up colleagues how mirror worlds are used in practice today.
Resources
Lecture Notes
Lecture slides and related material (software codes) will be made available in digital form (Moodle, Website & GitHub Repository).
General Information
- Language
- English
- Levels
- BSC , MSC
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise | Agent Based Modeling in Transportation | No time listed | 4 h weekly |
Offered In
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Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed.)
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Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed. All courses listed as core courses (not electives) for one of the following ETH MSc programmes, MSc Statistics, MSc Physics, MSc Computer Science, MSc (Applied) Mathematics, MSc Neural Systems and Computation, MSc Robotics, Systems, and Control, MSc Data Science, MSc Electrical Engineering and Information Technology, can be taken as an elective course in the MSc CSE without prior permission.)
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Modeling and Statistical Analysis (Tthe course 701-1565-00 Quantitative Policy Analysis and Modeling is compulsory)
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