Biography

Dr. K.W. Axhausen is Professor emeritus of Transport Planning at the Eidgenössische Technische Hochschule (ETH) Zürich. He is a member of the German Academy of Engineering (Acatech). Before he worked at the Leopold-Franzens Universität, Innsbruck, Imperial College London and the University of Oxford. He has been involved in the measurement and modelling of travel behavior for the past 40 years contributing especially to the literature on stated preferences, micro-simulation of travel behaviour (see www.matsim.org), accessibility, valuation of travel time and its components, parking behavior, activity scheduling, social networks and travel diary data collection including also GPS tracking. His Current work focusses on the e-bike-city project (https://ebikecity.baug.ethz.ch/).

Title: Is there a way out of the dilemma of transport planning

As long as we take our GHG reduction targets seriously, policy will have to aim at the reduction of GHG intensive mode use, i.e. private car use. The dilemma is the possible associtated loss of accessibility, the productivity enhancing product of the transport system. The presentation will discuss one possible vision to achieve the goals while not losing the accessibility benefits: A city redesigned for cycling and ebikecycle use. Based on the Zürich case, it will present the designs and the resulting impacts as measured by agent-based simulation.

Biography

Emma Frejinger is a professor in the Department of Computer Science and Operations Research at Université de Montréal where she holds a Canada Research Chair and an industrial chair funded by the Canadian National Railway Company. Her research is application-driven and focuses on innovative combinations of methodologies from machine learning and operations research to solve large-scale decision-making problems. Emma has extensive experience leading collaborative research projects and working with industry, predominantly within the transportation sector. She serves as a scientific advisor for IVADO Labs, an AI solution provider; as an academic affiliate with Analysis Group; and as an associate member of the machine learning institute Mila. Before joining Université de Montréal in 2013, Emma was a faculty member at KTH Royal Institute of Technology in Sweden. She holds a Ph.D. in mathematics from EPFL (Switzerland).

Title: Towards Integrated Learning and Optimization for Efficient Transport Systems

Decision makers are often faced with problems that are subject to uncertainty. Consider the problem of planning transport services for an upcoming season, determining optimal locations of new infrastructure, or establishing pricing strategies. In this context, demand uncertainty is challenging to deal with, notably because it is decision-dependent. In this talk, we discuss data and modeling challenges associated with understanding and predicting demand. Focusing on the competitive facility location problem, we describe a methodology to deal with decision-dependent demand uncertainty without making strong distributional assumptions. We also provide a high-level overview of contextual stochastic optimization. Studied in the literature under a variety of names, contextual optimization refers to data-driven approaches to prescribe decisions by exploiting relevant side information. We position demand modeling for decision-making in this context and outline future research directions on integrated learning and optimization.

Biography

Dr. Yafeng Yin is the Donald Cleveland Collegiate Professor of Engineering and the Donald Malloure Department Chair of Civil and Environmental Engineering at the University of Michigan, Ann Arbor. His research focuses on analyzing and improving multimodal transportation systems to enhance efficiency, resilience, and environmental sustainability. He is currently working on developing innovative mobility solutions and services through advancements in vehicle connectivity and automation. Dr. Yin has authored over 150 peer-reviewed papers in leading academic journals. He served as Editor-in-Chief of Transportation Research Part C: Emerging Technologies from 2014 to 2020 and currently holds editorial roles as an Area Editor for Transportation Science and an Associate Editor for Transportation Research Part B: Methodological. His contributions have been recognized with awards such as the Monroe-Brown Foundation Education Excellence Award from the University of Michigan's College of Engineering and the Doctoral Mentoring Award from the University of Florida. He has also received the Outstanding Leadership Award from the Chinese Overseas Transportation Association (COTA) and several paper awards, including the Stella Dafermos Best Paper Award, the Ryuichi Kitamura Paper Award, and the Kikuchi-Karlaftis Best Paper Award from the Transportation Research Board of the National Academies of Sciences, Engineering, and Medicine. Dr. Yin earned his Ph.D. from the University of Tokyo in 2002 and his master’s and bachelor’s degrees from Tsinghua University in 1996 and 1994, respectively.

Title: Leveraging Vehicle Trajectories for Traffic Network Equilibrium Modeling

Recent advances in connected vehicle technology and crowd-sourced navigation platforms have generated abundant vehicle trajectory data. This talk introduces a scalable, trajectory-based system for diagnosing and improving urban traffic networks. Leveraging vehicle trajectory and open-source data, we construct traffic network equilibrium models by calibrating route choice behavior, link performance functions, and origin-destination trip demand. These "light-duty" models are suited for analyzing traffic networks and prescribing short-term transportation improvement projects or policies. Rather than aiming for absolute predictive accuracy, the system emphasizes decision-relevant accuracy to ensure effective and actionable insights. By minimizing reliance on costly local data collection, this approach democratizes access to advanced traffic diagnostics. The resulting system provides a practical and deployable tool for data-driven traffic network analysis and decision-making.