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Winter School 2026 (Beijing): Smart Sustainable Cities and Transportations

Date
28 November – 4 December 2026

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 2026 (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 of training courses spanning from 28 November – 4 December 2026, 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 2026 (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

Travel Choices Under Uncertainty And Information Provision

Travelers increasingly make decisions under uncertainty arising from congestion, disruptions, and emerging mobility technologies. This module first examines travel choices through expected utility theory and then considers departures from fully rational decision-making. It further explores how information provision influences route, departure-time, and mode choices, including emerging settings where service robots communicate and personalize information. Applications demonstrate how behavioral models can support effective information strategies and resilient mobility management.

Lecturer: Dr. Zhenyu Yang, Technical University of Munich (TUM), Research Group Leader, Chair of Traffic Engineering and Control

Markets For Sustainable Cities: Tradable Credits In Demand Management

This module introduces Tradable Credit Schemes (TCS) as an innovative, market-based instrument for urban demand management. Students will examine core TCS principles, operational characteristics, and advantages over conventional congestion pricing, highlighting equity, public acceptability, and revenue neutrality. The module reviews current research and practice, pinpointing open questions in behavioral response, market design, and ethical challenges. Connecting theory to practice, the course highlights ongoing research at TUM, including agent-based simulation frameworks, empirical field experiments, and emerging LLM persona-based interactive environments for evaluating traveler decisions.

Lecturer: Dr. Anke Ye, Technical University of Munich (TUM), Postdoctoral Researcher, Chair of Traffic Engineering and Control

AI For Transport Resilience And Vulnerability

Transport systems are increasingly exposed to disruptions, from adverse weather and infrastructure failures to service unreliability, that unevenly affect network performance and the populations who structurally depend on it. In this module we look at two complementary sides of this challenge: how artificial intelligence can be used to forecast and manage disruption-induced traffic dynamics across complex networks, and how data-driven indices and clustering methods can be used to map where structural vulnerability and system unreliability spatially coincide. Applications from real transport networks will be used to demonstrate how these methods generate actionable insights for building more resilient and equitable transport systems.

Lecturer: Mr. Hamza Begic, Technical University of Munich (TUM), Research Associate, Chair of Transportation Systems Engineering

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, Technical University of Munich Asia, Faculty Head and Principal Investigator (Rail, Transport & Logistics)

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: Professor 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

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

Discrete Choice Models Through The Lens Of Modern Optimization And Machine Learning

This module introduces discrete choice models and examines how modern optimization and machine learning address key modeling challenges. Topics include robust estimation under data uncertainty, exact sparsity for utility specification, large language models for travel behavior prediction with limited labeled data, and scalable estimation with TorchDCM. Mathematical formulations, solution methods, and empirical applications illustrate how these approaches support interpretable and efficient choice modeling.

Lecturer: Dr. Baichuan Mo, Assistant Professor, Department of Transportation Engineering, Tsinghua University

AI-Empowered Intelligent Transportation Planning Methods And Applications

This module introduces discrete choice models and examines how modern optimization and machine learning address key modeling challenges. Topics include robust estimation under data uncertainty, exact sparsity for utility specification, large language models for travel behavior prediction with limited labeled data, and scalable estimation with TorchDCM. Mathematical formulations, solution methods, and empirical applications illustrate how these approaches support interpretable and efficient choice modeling.

Lecturer: Dr. Yuebing Liang, Tsinghua University, Assistant Professor, Department of Urban Planning

Fundamental Traffic Simulation Modelling With VISSIM

Road traffic is characterized by inherent dynamic uncertainty, and microscopic simulation offers a powerful tool for traffic scheme evaluation. This module introduces the complete basic VISSIM modelling workflow, including network construction, traffic flow definition, signal timing configuration, parameter calibration and simulation result analysis. It elaborates core car-following and lane-changing behavioral models behind the software. Practical case studies illustrate standard modelling practices to build solid simulation foundations for traffic assessment and resilient mobility management.

Lecturer: Feng XU, Senior Technical Consultant, PTV Software Technology (Shanghai) Co., Ltd.

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.