Blog

New member in our group: Aditya

We are very happy to welcome Aditya Paul to the DTU – Intelligent Transportation Systems as a new Postdoctoral Researcher! 🎓🚶‍♂️🛳️

Aditya has joined the Intelligent Transport Systems section as a Postdoctoral Researcher working on the Horizon FEDORA – EU Project. This project focuses on developing a federated platform for multimodal traffic management to enable agile, secure, and resilient transport services across diverse European contexts. Aditya’s work contributes to the project’s goal of creating a collaborative space for advanced optimization and the sensing of supply and demand to facilitate optimal transport operations.

Aditya holds a PhD from UNSW in Australia, where his research specialized in data-driven optimization for dynamic and stochastic transport and logistics. During his doctoral studies, he integrated supervised machine learning into mixed-integer linear programs to prevent the myopic resource allocation typical of conventional static models.

We’re delighted to have Aditya on board and wish him a pleasant and productive time with us at DTU! 😊

Visit the FEDORA project website here! 🚈

New member in our group: Junbo

We are excited to welcome Junbo Xia to DTU – Intelligent Transportation Systems as a new PhD student!

Junbo is working on the Transform MSCA-DN project, under the supervision of Associate Professor Carlos Lima de Azevedo and Professor Sonja Haustein (Human Mobility Group – DTU). The TRANSFORM project, funded by the Marie Skłodowska-Curie Actions, explores how urban mobility cultures evolve over time, what drives these changes, and how they shape our cities and transport systems.

Within this project, Junbo will investigate how transport policies and new technologies shape urban mobility cultures across Europe, using open urban datasets and machine-learning methods to analyse mobility transitions.

Junbo holds a Master’s degree in Urban Planning from the National University of Singapore, where he focused on urban mobility, built environment studies, and urban data analytics. His strong background in environmental and mobility-related topics will be a great asset to our research activities.

We’re delighted to have Junbo on board and wish him a pleasant and productive time with us at DTU! 😊

Please join us in welcoming him to DTU!

New member in our group: Miriam

We are excited to welcome Miriam Pasternak Jorgensen to DTU – Intelligent Transportation Systems as a new PhD student!

Miriam’s PhD sits at the intersection of computational social science and security studies. Her research explores how AI-enabled agent-based modelling can enhance traditional analytic methods, in close collaboration with several Danish universities. She is supervised by Associate Professor Filipe Rodrigues (DTU) and Associate Professor Kira Vrist Rønn (University of Southern Denmark).

Before joining DTU, Miriam worked as a policy analyst at the RAND Corporation and holds a Master’s degree in Security Studies from Georgetown University in Washington, D.C.

We’re very happy to have Miriam on board and look forward to the contributions she will bring to our research community and projects.
Please join us in welcoming her to DTU!

New members in our group: Welcome Johanne, Isabela and…

Johanne Conradsen, Isabela Maranca, and Lanlan Yan have joined our group as PhD students, working on the ‘IMMERSION’ project, under the supervision of Associate Professor Rico Krueger. Their work in IMMERSION aims to advance the study of human decision-making by developing new innovative methods for combining choice and process data. This research includes new models for integrating choice and process data, new statistical inference procedures tailored to such models, and new methods for collecting rich behavioural data in immersive experiments.

Johanne recently completed her MSc in Mathematical Modelling and Computation at DTU and brings a strong analytical background to our interdisciplinary team. In the IMMERSION project, Johanne will work on the development of new approaches for the flexible and scalable modelling of human decision-making using choice and process data.

Isabela recently completed her MSc in Sustainable Urban Mobility Transitions at the Universitat Politècnica de Catalunya and KTH Royal Institute of Technology. She will investigate immersive and non-immersive methods for collecting choice and process data, with a focus on understanding human decision-making in future transport systems.

Lanlan joins us after completing her MSc in Transport and Planning at Delft University of Technology. Her work in IMMERSION will centre on developing new methods for combining choice and process data to better explain and predict human decision-making.

Silke Kaiser Wins Best Paper Award at ECML PKDD…

The Intelligent Transportation Systems (ITS) group at DTU is pleased to share that Silke Kaiser, Doctoral Researcher in Machine Learning and Sustainable Transport, has received the Best Paper Award at the SoGood Workshop during the ECML PKDD 2025 conference in Porto, Portugal.

Her paper, “Spatio-Temporal Graph Neural Network for Urban Spaces: Interpolating Citywide Traffic Volume”, presents a novel approach to modeling and interpolating citywide traffic volume using spatio-temporal graph neural networks. This research contributes to advancing data-driven methods for understanding and managing urban mobility.

This work was partially conducted during Silke Kaiser’s research visit to the ITS group at DTU, reflecting the value of international collaboration and exchange of ideas in advancing transportation research.

The paper is available on arXiv:
🔗 https://lnkd.in/eHVA3RP5

We extend our congratulations to Silke Kaiser and her co-authors Filipe Rodrigues, Carlos Lima de Azevedo, and Lynn Kaack for this significant recognition.

New Ph.D. graduation in our group: congrats Atefeh!

On November 21st, 2024, Atefeh Hemmati Golsefidi successfully defended her Ph.D. thesis entitled “Adaptive and Flexible Charging Network Expansion with Reinforcement Learning, Optimization and Simulation“.

Congratulations, Atefeh!

She was supervised by Professor Francisco Camara Pereira and Associate Professor  Samitha Samaranayake (Cornell University).

Examiners were Professor Bissan Ghaddar (DTU Management), Associate Professor Andrea Araldo (Institut Polytechnique de Paris – Telecom SudParis) and Assistant Professor Mauro Salazar (Eindhoven University of Technology).

The defense session was chaired by Associate Professor Filipe Rodrigues.

For more information, feel free to contact Atefeh via her Linkedin

Seminar by Ricardo Daziano

We had the pleasure of hosting Ricardo Daziano, Associate Professor at Cornell University, for an insightful seminar on Neural Networks for Choice Analysis: Enhancing Behavioral Regularity and Interpretability.

Professor Daziano discussed innovative approaches to integrating machine learning with econometric principles, focusing on improving behavioral consistency in travel demand forecasting and incorporating network effects into discrete choice models using Graph Neural Networks (GNNs).

Below you can find the abstract and more information about Ricardo’s research interests.

Abstract: This talk explores the integration of machine learning with econometric principles, focusing on behavioral regularization in travel demand modeling. Two case studies will illustrate the need for combining behavioral regularization with advanced machine learning techniques to ensure that models are both theoretically sound and practically useful in travel decision-making contexts. The first case study examines monotonicity in travel demand forecasts from deep neural networks (DNNs). Despite the predictive strength of DNNs, predictions can fail to maintain behavioral consistency. New metrics for assessing monotonicity will be discussed, alongside a constrained optimization framework that exploits gradient regularizers. Applied to travel survey data from Chicago and London, the proposed approach improves DNN behavioral regularity while preserving predictive accuracy, with notable enhancements in smaller samples and out-of-domain scenarios. The second case study introduces a novel graph neural network (GNN) architecture designed to incorporate network effects into discrete choice models, while keeping parametric interpretability. Traditional discrete choice models typically overlook or neglect peer influence, but the proposed GNN approach effectively integrates peer effects, resulting in superior predictive performance and greater interpretability. When applied to New York City commuting data, the GNN model provides valuable economic insights, such as the value of time (VOT), demonstrating its effectiveness in capturing network effects and offering robust policy implications.

Bio: Ricardo A. Daziano, PhD in Economics and Associate Professor of Civil and Environmental Engineering at Cornell University, is a choice modeler working on deriving Bayesian and semiparametric estimators for microeconometric models of customer decisions in engineering contexts. Daziano’s research has a unique focus on applied econometrics of consumer choices around energy efficiency. His technical goal is to derive and apply preference estimators that are econometrically, behaviorally, and computationally superior to extant tools. His empirical goal is to better understand the interaction of consumer behavior and engineering, investment, and policy choices for energy-efficient technologies and sustainability, especially in transportation. In his transdisciplinary career, Daziano has been particularly interested in modeling the socio-technical transition to electric vehicles (EVs), and has used non-market valuation techniques to ascertain preferences for EV features, as well as preferences for EV charging and electricity contracts.