Blog

New member in our group: welcome Lorena!

Lorena Torres Lahoz recently joined our group as a Ph.D. student within the eMOTIONAL Cities project, where she will focus on the inclusion of emotions in discrete choice model frameworks using machine learning and scenario discovery. She will be working under the supervision of Professors Carlos Lima Azevedo and Francisco Camara Pereira.

She has graduated from DTU and Universidad Politécnica de Valencia with a double MSc degree in Mathematical modeling and computation.

We wish her best of luck on her PhD journey! 😊

MLSM Group at ICMC 2022: 6 papers

The 7th International Choice Modelling Conference (ICMC) will be held in Reykjavik, Iceland from 23-25 May 2022. Our group will contribute with six interesting papers. Check out below for more details.

New member in our group: welcome Mohamed!

Mohamed Eldafrawi is a guest Ph.D. candidate from Sapienza University of Rome and he will be working under the supervision of Prof. Francisco Pereira.

He mainly focuses on developing modeling frameworks for Dynamic Demand estimation and forecasting for real-world traffic modeling and ride-sourcing applications, combining those model-based frameworks with state-of-the-art data-driven methodologies and extracting the hidden transportation domain knowledge from multi-source and heterogenous traffic data.

We wish him a productive and fun time during his stay with us! 😊

New member in our group: welcome Rico!

Rico Krueger joined our group as an assistant professor on 1 February 2022. His research focusses on the development of methods at the intersection of machine learning, econometrics and statistics for the study of human behaviour in the context of transport and mobility.

Prior to joining DTU, he was a postdoc in the Transport and Mobility Laboratory (TRANSP-OR) at the Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He holds a PhD in Civil and Environmental Engineering from the University of New South Wales in Sydney, Australia.

We wish him a productive and fun time during his stay with us! 😊

New member in our group: welcome Santhanakrishnan!

Santhanakrishnan Narayanan is a guest Ph.D. candidate from the Technical University of Munich (TUM) and he will be working under the supervision of Prof. Carlos Lima Azevedo.

He majorly focusses on developing modelling frameworks for emerging transport modes, with an emphasis on using state-of-the-art data-driven methodologies and new data sources such as smart card and mobile phone data. His research spans across the fields of conventional & automated shared mobility services in passenger transport and cargo cycles in commercial transport.½

We wish him a productive and fun time during his stay with us! 😊

MLSM Group at TRB2022: 6 papers

101th Transportation Research Board Annual Meeting (TRB 2022) will be held in Washington, D.C. from 9-13 January 2022. Our group will contribute with six interesting papers. Check out below for more details.

New member in our group: welcome Giovanni!

Giovanni Tuveri is a guest Ph.D. student from the University of Cagliari (Italy) and he will be working under the supervision of Prof. Francisco.

Currently, Giovanni is working on neural networks applications to mode-choice in transportation when attitudinal variables are included in the inputs. His interests include novel applications of machine learning algorithms as valid alternatives to discrete choice models.

We wish him a productive and fun time during their stay with us! 😊

New member in our group: welcome Carolin!

Carolin Schmidt recently joined our group as a Ph.D. student within the SHOW project (SHared automation Operating models for Worldwide adoption), where she will focus on machine learning for shared, connected, and cooperative automated vehicles.

Previously, she graduated from RWTH Aachen University with an MSc in electrical power engineering and operations research.

We wish her a productive and fun time during their stay with us! 😊

New member in our group: welcome Yunhai Gong!

Yunhai Gong is our new guest Ph.D. student from the Dalian University of Technology (China), being co-supervised by Profs. Shaopeng Zhong and Carlos Lima Azevedo.

At present, Yunhai Gong mainly focuses on Large-scale traffic network signal control optimization. His research interests mainly include computer vision and traffic flow, traffic simulation, network traffic signal optimization, as well as applications of deep learning and reinforcement learning in the field of transportation. 

We wish him a productive and fun time during his stay with us! 😊

Our recent PhD graduate won the RA’s Research Prize…

Niklas Petersen, who recently completed his PhD in our group, won the RA’s – Rutebilvognmændenes Arbejdsgiverforenings – Research Prize at the Public Transport Conference 2021 for his PhD thesis entitled “Multi-model bus arrival prediction with intelligent handling of uncertainties“. Congrats, Niklas!

To read the full story click here.

Find below the abstract of Niklas’ thesis.

Waiting for the bus will presumably never be exciting. However, digital departure screens and smartphone apps (e.g. Rejseplanen) now allow the traveler to keep track and know when their bus is arriving. At least that is how it is supposed to work, but in reality, many have experienced a digital departure screen reaching “zero” and yet no bus was there. Behind the scenes, each bus is constantly sending its current location based on GPS to a central prediction system, that tries to guess all the arrivals of each and every bus. But it is a difficult task with varying congestion in the cities, unpredictable traffic signals, road works, and incidents. This Ph.D. firstly explores how machines can model and learn better to guess more accurately using the advancements in computational power and data available. But there is a catch: the more complex machine learning techniques do not always make sense, and they are prone to fail when their underlining assumptions are not met. Therefore, secondly, a multi-model approach is presented where multiple, both simple and complex, machine learning techniques can compete and support each other for enhancing robustness, flexibility, and accuracy in a real-world prediction system. The presented multi-model approach is shown to outperform single-model approaches. The thesis also provides two studies on how the combination of location data from GPS and machine learning technology can be used together to extract valuable information about travel behavior, including an example of measuring the walking time needed when travelers are transferring from a bus to a train in the public transport system.