Francesco Corman: Katalogdaten im Herbstsemester 2022

NameHerr Prof. Dr. Francesco Corman
LehrgebietTransportsysteme
Adresse
Professur für Transportsysteme
ETH Zürich, HIL F 13.1
Stefano-Franscini-Platz 5
8093 Zürich
SWITZERLAND
Telefon+41 44 633 33 50
E-Mailfrancesco.corman@ivt.baug.ethz.ch
DepartementBau, Umwelt und Geomatik
BeziehungAusserordentlicher Professor

NummerTitelECTSUmfangDozierende
101-0427-01LPublic Transport Design and Operations6 KP4GF. Corman, T.‑H. Yan
KurzbeschreibungThis course aims at analyzing, designing, improving public transport systems, as part of the overall transport system.
LernzielPublic 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.
InhaltBasics 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
SkriptLecture slides are provided.
LiteraturCeder, 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)
KompetenzenKompetenzen
Fachspezifische KompetenzenKonzepte und Theoriengeprüft
Verfahren und Technologiengeprüft
Methodenspezifische KompetenzenAnalytische Kompetenzengeprüft
Entscheidungsfindunggeprüft
Medien und digitale Technologiengefördert
Problemlösunggeprüft
Projektmanagementgefördert
Soziale KompetenzenKommunikationgeprüft
Kooperation und Teamarbeitgeprüft
Kundenorientierunggeprüft
Menschenführung und Verantwortunggefördert
Selbstdarstellung und soziale Einflussnahmegefördert
Sensibilität für Vielfalt gefördert
Verhandlunggefördert
Persönliche KompetenzenAnpassung und Flexibilitätgefördert
Kreatives Denkengeprüft
Kritisches Denkengeprüft
Integrität und Arbeitsethikgefördert
Selbstbewusstsein und Selbstreflexion gefördert
Selbststeuerung und Selbstmanagement gefördert
101-0522-10LDoctoral Seminar Data Science and Machine Learning in Civil, Env. and Geospatial Engineering Belegung eingeschränkt - Details anzeigen
Number of participants limited to 21.
1 KP1SM. J. Van Strien, E. Chatzi, F. Corman, I. Hajnsek, M. A. Kraus, M. Lukovic, V. Ntertimanis, K. Schindler, B. Soja
KurzbeschreibungCurrent 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.
Lernziel- 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
InhaltCurrent research at D-BAUG will be presented and discussed.
Voraussetzungen / BesonderesThis 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.
101-0523-13LFrontiers in Machine Learning Applied to Civil, Env. and Geospatial Engineering (HS22) Belegung eingeschränkt - Details anzeigen 1 KP1GM. J. Van Strien, E. Chatzi, F. Corman, I. Hajnsek, M. A. Kraus, M. Lukovic, V. Ntertimanis, K. Schindler, B. Soja
KurzbeschreibungThis 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).
LernzielStudents 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.
InhaltWith 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.
Voraussetzungen / BesonderesThis 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.
103-0377-10LBasics of RE&IS Belegung eingeschränkt - Details anzeigen
Nur für Raumentwicklung und Infrastruktursysteme MSc.
3 KP2GJ. Van Wezemael, K. W. Axhausen, F. Corman, C. Sailer
KurzbeschreibungThe course Basics of RE&IS provides essential knowledge for the Master's degree program in Spatial Development & Infrastructure Systems. It teaches the basics of technical-scientific work, such as scientific writing, literature review, and effective presentation and communication of results.
Lernziel-Students will be able to identify, name, and define the content taught and understand the necessity, significance, and application of the standards in scientific work.
-Students will be able to apply the content, implement it in different examples and use it to solve the exercises and the semester assignment.
-Students develop a common understanding with regard to their methodological knowledge and can henceforth work scientifically at an appropriate level.
-With the techniques learned in the course, students will be able to
•analyze and differentiate scientific sources and apply them in their work in a structured way
•systematically compare and present their results in an argumentative manner
•develop, formulate, and design a scientific report
•produce results in collaboration with their group
•present results in an engaging presentation with their group using attractive and formally correct visualizations, maps, or diagrams
•discuss and give critical feedback in the form of peer-assessments of other students
InhaltStudents will learn the basics of scientific work and practice their skills within the framework of three separate exercises (formative) as well as an ungraded semester performance, which consists of two parts and will be worked out in groups of two to three students.

In the first half of the semester, students will learn the theoretical basics and apply and understand these in the context of the exercises. In the second half of the semester, the students will work on a written scientific report applying the methods learnt in the first half of the semester. The results of the report should be communicated in an effective and clear oral presentation taped on video. The final videos, as well as the exercises in the first part of the course will be discussed and evaluated among the students in class (peer-assessment).

- Exercise 1: Literature search & referencing
- Exercise 2: Scientific writing – report structure, paragraph structure, language style
- Exercise 3: Maps, Graphs & Visualizations
- Ungraded semester performance: consists of (1) written report on topic of interest and (2) oral presentation on video

Students will be supervised by the course instructors throughout the course. Furthermore, feedback and discussion opportunities will be given by other students by the principle of peer assessment.
The main course lead changes periodically between the following RE&IS chairs: Infrastructure Management (IM), Transportation Systems (TS), Traffic Engineering (SVT), Transport Planning (VPL), Spatial Development and Urban Policy (SPUR), Planning of Landscape and Urban Systems (PLUS) and Spatial Transformation Laboratories (STL).
SkriptAll documents relevant for the course (slides, literature, further links, etc.) are provided centrally via the Moodle platform.
LiteraturAmerican Psychological Association (APA) (2010) Publication Manual of the American Psychological Association, 6th edition, APA, Washington, D.C.
Axhausen, K.W. (2016) Style Guide for Student Dissertations, IVT, ETH Zürich, Zürich (available as download under learning materials)
Backhaus, N. and R. Tuor (2008): Leitfaden für wissenschaftliches Arbeiten, 7. überarbeitete und ergänzte Auflage. Schriftenreihe Humangeographie 18, Geographisches Institut der Universität Zürich, Zürich.
ZürichChapman, M. and C. Wykes (1996) Plain Figures, HM Stationary Office, London.
ETH (2017) Citation etiquette: How to handle the intellectual property of others, ETH, ETH Zürich, Zürich (last retrieved 29.11.2017)
Modern Language Association of America (MLA) (2016) MLA Handbook, 8th edition, MLA, New York.
Monmonier, M. (1991) How to lie with maps, University of Chicago Press, Chicago.
Tufte, E. R. (2001) The Visual Display of Quantitative Information, Graphics Press USA
Wilkinson, L. (1999) The Grammar of Graphics, Springer, Berlin.
364-1058-00LRisk Center Seminar Series0 KP2SH. Schernberg, D. Basin, A. Bommier, D. N. Bresch, S. Brusoni, L.‑E. Cederman, P. Cheridito, F. Corman, H. Gersbach, C. Hölscher, K. Paterson, G. Sansavini, B. Stojadinovic, B. Sudret, J. Teichmann, R. Wattenhofer, U. A. Weidmann, S. Wiemer, M. Zeilinger, R. Zenklusen
KurzbeschreibungThis 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.
LernzielParticipants 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.
InhaltThis 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.
SkriptThere 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.
LiteraturLiterature will be provided by the speakers in their respective presentations.
Voraussetzungen / BesonderesParticipants should have relatively good mathematical skills and some experience of how scientific work is performed.