Date
8 – 14 December 2025
Venue
Tsinghua University
Haidian District, Beijing, 100084, P. R. China
Format
In-person
Learn from some of the world’s top universities this winter.
Embark on an exhilarating learning adventure this winter with the exclusive Winter School 2025 (Beijing) conducted two of the top universities in the world. Featuring an exciting line up of professors from TUM, TUM Asia and Tsinghua University.
Comprising a total of 13 sessions of training courses spanning from 08 – 14 December 2025, this Winter School features a cadre of experts from both universities to present research on Smart Sustainable Cities and Transportation.
Eligibility Criteria
Applications are open to candidates who already hold a bachelor’s degree or who are currently enrolled in a bachelor’s degree programme, in any of the following areas (but not limited): Civil Engineering, Transportation Engineering, Electrical Engineering, Geodetics, Mechanical Engineering, Geography, Computer Science, Communications Engineering, Economics, Mathematics, Physical Sciences, Architecture, Environmental Engineering, Tourisms.
All classes at the Winter School 2025 (Beijing) will be conducted in English. Participants should ensure that they are proficient in English (reading and writing) at university level.
Key Topics
- Smart and green city growth
- Use of geospatial data and geoinformatics to plan and manage smart city development and organize the transport system
- Usage of big data analytics and artificial intelligence,
- Usage if drones and robots in city and transport planning and management
- Smart Railway systems and integration of public transport modes
Data-Driven Transportation Models
The emergence of novel data collection techniques has enabled a paradigm shift from primarily theory-based models to models that also exploit emerging data to develop more flexible, comprehensive functional forms. Machine learning methods are increasing in complexity and combined with the richer data can result in more powerful models. In this module we provide an overview of methodologies and related applications, such as resilience and emissions modelling, highlighting the state-of-the-art in the field of data-driven transportation models.
Lecturer: Prof. Dr. Constantinos Antoniou, TUM, Chair of Transportation Systems Engineering
Modelling Traveler Behaviour
The transportation network performance is the result of the interaction between demand and supply. In this module we will present models and applications of modelling traveller behaviour in order to be able to quantify the demand for transportation. We will look at different data collection techniques (traditional surveys, but also opportunistic data sources), and different modelling techniques. Applications from several real use cases and projects will be used to demonstrate the models and link them with policy and practice.
Lecturer: Prof. Dr. Constantinos Antoniou, TUM, Chair of Transportation Systems Engineering
Lecturer: Prof. Dr. Constantinos Antoniou, TUM, Chair of Transportation Systems Engineering
Smart Capacity Management for Railway Systems
Rail Transportation Systems are the backbone of urban and continental transportation. In view of the rail renaissance and continuously growing demand experienced in recent years existing networks are operating under increasing stress. Transport planners and operators alike are therefore looking for ways to further increase the traffic load that can be supported by existing systems. In light of these developments the course will discuss different concepts and methodologies to define and assess the capacity of rail transportation systems. Starting from signalling and traffic control aspects, we will discuss approaches for robust capacity allocation, timetable stability evaluation, and network performance analysis and review new technologies in traffic management and train control with respect to their ability to further increase the efficiency and reliability of rail services.
Lecturer: Prof. Dr. Norman Weik, TUM, Professor for Design and Operation of Public Rail Transport Systems
Lecturer: Prof. Dr. Norman Weik, TUM, Professor for Design and Operation of Public Rail Transport Systems
Sustainable Urban planning concepts
City development and transport system are connected. This module will show how both are linked to each other and what kind of policies should be used to create a city with less congestion and a high living quality. Examples from cities worldwide will be shown. The principles and strategies of transport demand management will be presented. What are suitable policies to reduce the traffic volume on the roads and give priority for traffic which can’t be avoided and generates the highest benefits / revenue for the society? The last part of the presentation will focus on the implementation of these theoretical concepts into a real transport policy using Singapore as an example.
Lecturer: Dr. Andreas Rau, TUM Asia, Faculty Head and Principal Investigator (Rail, Transport & Logistics)
Lecturer: Dr. Andreas Rau, TUM Asia, Faculty Head and Principal Investigator (Rail, Transport & Logistics)
Semantic Modeling of the Built Environment for Digital Twins
The rapid pace of urbanization and the growing demand for sustainable and intelligent cities have amplified the need for accurate and semantically rich modeling of the built environment. This tutorial provides a comprehensive overview of contemporary approaches to digital representation of buildings, infrastructure, and urban systems, emphasizing the role of semantic modeling as a foundation for Digital Twins.
The session introduces the two major paradigms of built environment modeling—Building Information Modeling (BIM/IFC) and Geographic Information Systems (GIS/CityGML)—highlighting their distinct purposes, data structures, and reference frameworks. Participants will explore how these two modeling domains complement each other and why their integration is increasingly essential for applications spanning the building-to-city scale.
Through theoretical discussion and practical examples, the tutorial will examine and compare the semantic, geometric, and reference system differences between IFC and CityGML. It will further explore integration approaches, including data transformation, schema mapping, and model linking techniques.
Application scenarios, such as indoor navigation, utility network modeling, and 3D urban data integration, will be demonstrated to illustrate the advantages and challenges of achieving interoperability between BIM and GIS environments.
By the end of the 5-hour tutorial, participants will gain a conceptual and practical understanding of semantic modeling principles for the built environment, an awareness of existing standards and tools, and insight into ongoing research and implementation trends that drive the development of Digital Twins at both building and city scales.
Lecturer: Dr. Ihab Hijazi, TUM, Chair of Geoinformatics
The session introduces the two major paradigms of built environment modeling—Building Information Modeling (BIM/IFC) and Geographic Information Systems (GIS/CityGML)—highlighting their distinct purposes, data structures, and reference frameworks. Participants will explore how these two modeling domains complement each other and why their integration is increasingly essential for applications spanning the building-to-city scale.
Through theoretical discussion and practical examples, the tutorial will examine and compare the semantic, geometric, and reference system differences between IFC and CityGML. It will further explore integration approaches, including data transformation, schema mapping, and model linking techniques.
Application scenarios, such as indoor navigation, utility network modeling, and 3D urban data integration, will be demonstrated to illustrate the advantages and challenges of achieving interoperability between BIM and GIS environments.
By the end of the 5-hour tutorial, participants will gain a conceptual and practical understanding of semantic modeling principles for the built environment, an awareness of existing standards and tools, and insight into ongoing research and implementation trends that drive the development of Digital Twins at both building and city scales.
Lecturer: Dr. Ihab Hijazi, TUM, Chair of Geoinformatics
Cycling Policies for a Sustainable City Development
This module aims to introduce the basic principles of public transport policies to create transport system where people can rely on a fast and passenger friendly public transport system. The theoretical policies will be supported by showing some case studies in Chinese cities, where these concepts are applied in a real city.
Lecturer: Prof. Dr. Yang Xinmiao, Tsinghua University
Lecturer: Prof. Dr. Yang Xinmiao, Tsinghua University
Transport Planning Theory and Its Development
This course is to introduce the basic principles of transport planning, planning methods, the working process of transport planning, and the application of transport planning theory in various transport planning and the latest research progress. The main contents include the characteristics of traffic demand and the main influencing factors, the content and main processes of urban traffic planning, the classical methods of urban traffic demand forecasting, and the outlook of traffic planning and management theory research.
Lecturer: Prof. Dr. Huapu Lu, Tsinghua University
Lecturer: Prof. Dr. Huapu Lu, Tsinghua University
Urban Artificial Intelligence: Theory, Methodology and Applications
Contemporary cities are undergoing the fourth industrial revolution, characterized by disruptive technologies like artificial intelligence (AI). Urban Artificial Intelligence (UrbanAI) represents the convergence of AI with urban disciplines. This presentation outlines a structured UrbanAI framework utilizing cutting-edge technologies including AIGC, LLMs, IoT, and robotics, built upon three key dimensions: analyzing urban dynamics through AI, transforming urban spaces via AI applications, and innovating urban environments with AI capabilities. First, UrbanAI is revolutionizing urban studies through novel data collection and analysis methods. Second, it’s reshaping urban life and spatial structures, thereby updating traditional urban theories. Finally, UrbanAI serves as a new productivity tool, enhancing urban planning, management, and construction. The discussion highlights UrbanAI’s trajectories, potential, and implications for urban science and practice, while addressing both opportunities and challenges in UrbanAI development.
Lecturer: Prof. Dr. Ying Long, Tsinghua University
Lecturer: Prof. Dr. Ying Long, Tsinghua University
Autonomous Driving: From Origins to Future Frontiers
Autonomous driving is one of the most transformative technological frontiers of our time, reshaping the way humans perceive mobility, safety, and intelligent systems. This lecture provides an in-depth introduction to the evolution of autonomous driving, tracing its origins from early rule-based systems developed in almost 40 years ago, to today’s AI-driven approaches. We will explore the fundamental milestones that have shaped the field, including breakthroughs in sensor technologies, computer vision, and decision-making methods, as well as the emergence of large-scale data-driven methods. Special emphasis will be placed on the role of artificial intelligence (AI), covering key techniques used in sensing, perception, prediction, planning, and how these components are gradually merging and evolving into the end-to-end systems that are being used in modern self-driving cars. At the same time, we will examine the practical challenges that remain unsolved, such as safety assurance, generalization across diverse scenarios. Finally, the lecture will highlight recent advances in autonomous driving, including end-to-end autonomous driving, using frontier generative AI for autonomous driving planning, as well as the most recent large vision-language-action (VLA) models for autonomous driving. Through this journey, we hope students will get a broad yet insightful perspective on how autonomous driving technologies are developed, what barriers still need to be overcome, and how the field may shape the future of mobility and society.
Lecturer: Dr. Xianyuan Zhan, Tsinghua University
Lecturer: Dr. Xianyuan Zhan, Tsinghua University
Traffic Condition Estimation and Control
Nowadays, with the development of data collection, analysis and application technology, various kinds of abundant data bring new opportunities for urban road traffic state estimation and control. Based on multi-source data collection, this module introduces some research achievements of the lecturer in the field of multi-level traffic state estimation and control in recent years from the perspectives of macro-traffic state analysis, estimation, micro-traffic parameter estimation and feedback closed-loop traffic control.
Lecturer: Prof. Dr. Ruimin Li, Tsinghua University
Lecturer: Prof. Dr. Ruimin Li, Tsinghua University
Admission Criteria
Admission to our Winter School is highly competitive. Selection is based on a comprehensive review of all documents received in the application by the examination board. Candidates will be notified by email as soon as the admission is granted.
Participation Fees
Participation in the Winter School is complimentary and free of charge.
Submission Deadline: 15 November 2025 (Wednesday)
Contact Us
If you have any questions regarding the admission procedure, please email us at transportation.vtk@ed.tum.de.
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