Francesco Corman: Catalogue data in Autumn Semester 2021 |
| Name | Prof. Dr. Francesco Corman |
| Field | Transport Systems |
| Address | Professur für Transportsysteme ETH Zürich, HIL F 13.1 Stefano-Franscini-Platz 5 8093 Zürich SWITZERLAND |
| Telephone | +41 44 633 33 50 |
| francesco.corman@ivt.baug.ethz.ch | |
| Department | Civil, Environmental and Geomatic Engineering |
| Relationship | Associate Professor |
| Number | Title | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 101-0427-01L | Public Transport Design and Operations | 6 credits | 4G | F. Corman, F. Leutwiler | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course aims at analyzing, designing, improving public transport systems, as part of the overall transport system. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Public transport is a key driver for making our cities more livable, clean and accessible, providing safe, and sustainable travel options for millions of people around the globe. Proper planning of public transport system also ensures that the system is competitive in terms of speed and cost. Public transport is a crucial asset, whose social, economic and environmental benefits extend beyond those who use it regularly; it reduces the amount of cars and road infrastructure in cities; reduces injuries and fatalities associated to car accidents, and gives transport accessibility to very large demographic groups. Goal of the class is to understand the main characteristics and differences of public transport networks. Their various performance criteria based on various perspective and stakeholders. The most relevant decision making problems in a planning tactical and operational point of view At the end of this course, students can critically analyze existing networks of public transport, their design and use; consider and substantiate possible improvements to existing networks of public transport and the management of those networks; optimize the use of resources in public transport. General structure: general introduction of transport, modes, technologies, system design and line planning for different situations, mathematical models for design and line planning timetabling and tactical planning, and related mathematical approaches operations, and quantitative support to operational problems, evaluation of public transport systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Basics for line transport systems and networks Passenger/Supply requirements for line operations Objectives of system and network planning, from different perspectives and users, design dilemmas Conceptual concepts for passenger transport: long-distance, urban transport, regional, local transport Planning process, from demand evaluation to line planning to timetables to operations Matching demand and modes Line planning techniques Timetabling principles Allocation of resources Management of operations Measures of realized operations Improvements of existing services | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Lecture slides are provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Ceder, Avi: Public Transit Planning and Operation, CRC Press, 2015, ISBN 978-1466563919 (English) Holzapfel, Helmut: Urbanismus und Verkehr – Bausteine für Architekten, Stadt- und Verkehrsplaner, Vieweg+Teubner, Wiesbaden 2012, ISBN 978-3-8348-1950-5 (Deutsch) Hull, Angela: Transport Matters – Integrated approaches to planning city-regions, Routledge / Taylor & Francis Group, London / New York 2011, ISBN 978-0-415-48818-4 (English) Vuchic, Vukan R.: Urban Transit – Operations, Planning, and Economics, John Wiley & Sons, Hoboken / New Jersey 2005, ISBN 0-471-63265-1 (English) Walker, Jarrett: Human Transit – How clearer thinking about public transit can enrich our communities and our lives, ISLAND PRESS, Washington / Covelo / London 2012, ISBN 978-1-59726-971-1 (English) White, Peter: Public Transport - Its Planning, Management and Operation, 5th edition, Routledge, London / New York 2009, ISBN 978-0415445306 (English) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Competencies |
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| 101-0522-10L | Doctoral Seminar Data Science and Machine Learning in Civil, Env. and Geospatial Engineering  Does not take place this semester. Number of participants limited to 21. | 1 credit | 2S | B. Soja, E. Chatzi, F. Corman, O. Fink, I. Hajnsek, K. Schindler | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Current research in machine learning and data science within the research fields of the department. The goal is to learn about current research projects at our department, to strengthen our expertise and collaboration with respect to data-driven models and methods, to provide a platform where research challenges can be discussed, and also to practice scientific presentations. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | - learn about discipline-specific methods and applications of data science in neighbouring fields - network people and methodological expertise across disciplines - establish links and discuss connections, common challenges and disciplinespecific differences - practice presentation and discussion of technical content to a broader, less specialised scientific audience | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Current research at D-BAUG will be presented and discussed. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | This doctoral seminar is intended for doctoral students affiliated with the Department of Civil, Environmental and Geomatic Engineering. Other students who work on related topics need approval by at least one of the organisers to register for the seminar. Participants are expected to possess elementary skills in statistics, data science and machine learning, including both theory and practical modelling and implementation. The seminar targets students who are actively working on related research projects. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 101-0523-12L | Frontiers in Machine Learning Applied to Civil, Env. and Geospatial Engineering (HS21) Number of participants limited to 21. | 1 credit | 2S | M. A. Kraus, E. Chatzi, F. Corman, O. Fink, I. Hajnsek, M. Lukovic, K. Schindler, B. Soja, B. Sudret, M. J. Van Strien | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This doctoral seminar organised by the D-BAUG platform on data science and machine learning aims at discussing recent research papers in the field of machine learning and analyzing the transferability/adaptability of the proposed approaches to applications in the field of civil and environmental engineering (if possible and applicable, also implementing the adapted algorithms). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students will • Critically read scientific papers on the recent developments in machine learning • Put the research in context • Present the contributions • Discuss the validity of the scientific approach • Evaluate the underlying assumptions • Evaluate the transferability/adpatability of the proposed approaches to own research • (Optionally) implement the proposed approaches. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | With the increasing amount of data collected in various domains, the importance of data science in many disciplines, such as infrastructure monitoring and management, transportation, spatial planning, structural and environmental engineering, has been increasing. The field is constantly developing further with numerous advances, extensions and modifications. The course aims at discussing recent research papers in the field of machine learning and analyzing the transferability/adaptability of the proposed approaches to applications in the field of civil and environmental engineering (if possible and applicable, also implementing the adapted algorithms). Each student will select a paper that is relevant for his/her research and present its content in the seminar, putting it into context, analyzing the assumptions, the transferability and generalizability of the proposed approaches. The students will also link the research content of the selected paper to the own research, evaluating the potential of transferring or adapting it. If possible and applicable, the students will also implement the adapted algorithms The students will work in groups of three students, where each of the three students will be reading each other’s selected papers and providing feedback to each other. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | This doctoral seminar is intended for doctoral students affiliated with the Department of Civil, Environmental and Geomatic Engineering. Other students who work on related topics need approval by at least one of the organisers to register for the seminar. Participants are expected to possess elementary skills in statistics, data science and machine learning, including both theory and practical modelling and implementation. The seminar targets students who are actively working on related research projects. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 364-1058-00L | Risk Center Seminar Series | 0 credits | 2S | B. J. Bergmann, D. Basin, A. Bommier, D. N. Bresch, L.‑E. Cederman, P. Cheridito, F. Corman, O. Fink, H. Gersbach, C. Hölscher, K. Paterson, H. Schernberg, F. Schweitzer, D. Sornette, B. Stojadinovic, B. Sudret, J. Teichmann, U. A. Weidmann, S. Wiemer, M. Zeilinger, R. Zenklusen | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course is a mixture between a seminar primarily for PhD and postdoc students and a colloquium involving invited speakers. It consists of presentations and subsequent discussions in the area of modeling complex socio-economic systems and crises. Students and other guests are welcome. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Participants should learn to get an overview of the state of the art in the field, to present it in a well understandable way to an interdisciplinary scientific audience, to develop novel mathematical models for open problems, to analyze them with computers, and to defend their results in response to critical questions. In essence, participants should improve their scientific skills and learn to work scientifically on an internationally competitive level. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | This course is a mixture between a seminar primarily for PhD and postdoc students and a colloquium involving invited speakers. It consists of presentations and subsequent discussions in the area of modeling complex socio-economic systems and crises. For details of the program see the webpage of the colloquium. Students and other guests are welcome. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | There is no script, but a short protocol of the sessions will be sent to all participants who have participated in a particular session. Transparencies of the presentations may be put on the course webpage. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Literature will be provided by the speakers in their respective presentations. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Participants should have relatively good mathematical skills and some experience of how scientific work is performed. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||