Evangelos Paschalidis

Journal publications [2]

2023

Mastora C., Paschalidis E., Nikiforiadis A., Basbas S. (2023). Pedestrian Crossings as a Means of Reducing Conflicts between Cyclists and Pedestrians in Shared Spaces. Sustainability 15 (12)


@Article{MastPaschNikifBas,
  author = {Chrysanthi Mastora and Evangelos Paschalidis and Andreas Nikiforiadis and Socrates Basbas},
  title = {Pedestrian Crossings as a Means of Reducing Conflicts between

Cyclists and Pedestrians in Shared Spaces},
  journal = {Sustainability},
  year = {2023},
  volume = {15},
  number = {12},
  DOI = {https://doi.org/10.3390/su15129377}}}

Like J., Haibo C., Paschalidis E. (2023). Diffusion of connected and autonomous vehicles concerning mode choice, policy interventions and sustainability impacts: A system dynamics modelling study. Transport Policy 141 ()


@Article{JiangChenPasch,
  author = {Jiang Like and Chen Haibo and Evangelos Paschalidis},
  title = {Diffusion of connected and autonomous vehicles concerning mode choice, policy interventions and sustainability impacts: A system dynamics modelling study},
  journal = {Transport  Policy},
  year = {2023},
  volume = {141},
  DOI = {https://doi.org/10.1016/j.tranpol.2023.07.029}}}

Technical reports [1]

2023

Bierlaire M., Paschalidis E. (2023). Estimating MEV models with samples of alternatives. Report TRANSP-OR 231225


@TechReport{BierPasc24,
  author = {Michel Bierlaire and Evangelos Paschalidis},
  title = {Estimating MEV models with samples of alternatives},
  institution = {Transport and Mobility Laboratory, Ecole Polytechnique F\'ed\'erale de Lausanne},
  year = {2023},
  type = {Technical Report},
  number = {TRANSP-OR 231225},
  address = {Lausanne, Switzerland}}}

Articles in conference proceedings [1]

2023

Beine M., Bierlaire M., Paschalidis E., Vortisch A. (2023) Sampling of alternatives in migration aspiration models. Proceedings of the 23rd Swiss Transport Research Conference (STRC), 10-12 May, Ascona, Switzerland


@InProceedings{PaschalidisBierlaire_STRC2023,
  author = {Michel Beine and Michel Bierlaire and Evangelos Paschalidis and Andreas Vortisch},
  title = {Sampling of alternatives in migration aspiration models},
  booktime = {Proceedings of the 23rd Swiss Transport Research Conference (STRC), 10-12 May},
  year = {2023},
  address = {Ascona, Switzerland}}}

Talks [1]

2023

Beine M., Bierlaire M., Paschalidis E., Vortisch A. Sampling of alternatives in migration aspiration models May 10, 2023. 23rd Swiss Transport Research Conference (STRC), Ascona, Switzerland.


@Misc{2023Paschalidis_STRC,
  author = {Michel Beine and Michel Bierlaire and Evangelos Paschalidis and Andreas Vortisch},
  title = {Sampling of alternatives in migration aspiration models},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/2023Paschalidis_STRC.pdf}},
  year = {2023},
  note = {Presented at 23rd Swiss Transport Research Conference (STRC), Ascona, Switzerland}}}

EPFL courses [4]

Fall 2024

Mathematical modeling of behavior.
- Level: Master
- Sections: Mathematics, Master in Financial Engineering
- Lecturer: Michel Bierlaire
- Teaching assistants: Evangelos Paschalidis, Negar Rezvany, Cloe Cortes Balcells, Pavel Ilinov
- Webpage: https://moodle.epfl.ch/course/view.php?id=1001

Spring 2024

Decision-aid methodologies in transportation.
- Level: Master
- Section: Civil Engineering
- Lecturers: Fabian Torres, Evangelos Paschalidis
- Teaching assistant: Tom Haering

Fall 2023

Mathematical modeling of behavior.
- Level: Master
- Sections: Mathematics, Master in Financial Engineering
- Lecturer: Michel Bierlaire
- Teaching assistants: Evangelos Paschalidis, Nicola Ortelli, Janody Pougala, Cloe Cortes Balcells, Pavel Ilinov, Risa Kobayashi
- Webpage: https://moodle.epfl.ch/course/view.php?id=1001

Spring 2023

Decision-aid methodologies in transportation.
- Level: Master
- Section: Civil Engineering
- Lecturers: Fabian Torres, Evangelos Paschalidis
- Teaching assistants: Selin Atac, Tom Haering

Lectures [5]

2024

Bayesian Estimation; Individual Prediction.
- Discrete Choice Analysis: Bayesian estimation and Individual prediction, EPFL, Lausanne, Switzerland
- Feb 15, 2024
- Lecturer: Pavel Ilinov
- Assistants: Marija Kukic, Evangelos Paschalidis
Logit Mixtures; Combining Data (SP and RP).
- Discrete Choice Analysis: Predicting Individual Behavior and Market Demand, EPFL, Lausanne, Switzerland
- Feb 13, 2024
- Lecturer: Evangelos Paschalidis
- Assistants: Cloe Cortes Balcells, Nicola Ortelli
Nested logit; Aggregate forecasting.
- Discrete Choice Analysis: Predicting Individual Behavior and Market Demand, EPFL, Lausanne, Switzerland
- Feb 12, 2024
- Lecturer: Cloe Cortes Balcells
- Assistants: Pavel Ilinov, Evangelos Paschalidis

2023

Hybrid choice models.
- Discrete Choice Analysis: Predicting Individual Behavior and Market Demand, EPFL, Lausanne, Switzerland
- Feb 01, 2023
- Lecturer: Nicola Ortelli
- Assistants: Tom Haering, Evangelos Paschalidis, Janody Pougala
Logit Mixtures; Combining Data (SP and RP).
- Discrete Choice Analysis: Predicting Individual Behavior and Market Demand, EPFL, Lausanne, Switzerland
- Jan 31, 2023
- Lecturer: Evangelos Paschalidis
- Assistants: Cloe Cortes Balcells, Janody Pougala, Negar Rezvany

MSc theses [1]

2024

Application of the information-theoretic clustering to the discrete choice, Alexandre Monti (Mathematics)
- Supervision: Pavel Ilinov, Evangelos Paschalidis, Michel Bierlaire
- External expert:

Semester projects [3]

2024

Comparative analysis of discrete choice models estimation using different software packages, Alexia Stephanie Liviana Paratte

Supervision: Evangelos Paschalidis, Negar Rezvany, Michel Bierlaire
Since their establishment in the field of econometrics, discrete choice models have been extensively used in many research fields such as transportation, environmental economics, health, energy, marketing, and others. Currently, there is a plethora of commercial and open-source software that can be used for the estimation of discrete choice models. Biogeme (Bierlaire, 2003) and Apollo (Hess & Palma, 2019) are two of the most appealing alternatives, as they are both open-source (based on Python and R respectively) and cover a broad range of advanced discrete choice models and diagnostics. More recently, other alternatives have been developed such as mixl (Molloy et al., 2021) and MO|DE.behave (Reul et al., 2023). Given the differences in the code development and the tools used, this project aims at investigating (a) differences in performance and computational speed between the two packages, (b) potential differences in the results and model statistics for the same models and datasets, and (c) available functionalities i.e., what are the common features and what are the specific features of some packages that are not available in others. Understanding the pros and cons of each package via this comparative analysis can ultimately lead to more optimised and accurate software for estimating discrete choice models.

Improving the specification of migration aspiration discrete choice models via attitudinal variables, Anne-Valérie Preto (Civil Engineering)

Supervision: Evangelos Paschalidis, Nicola Ortelli, Michel Bierlaire
International migration is a crucial but controversial topic that raises issues and concerns to be resolved in the parties involved. In industrialised nations, the proportion of immigrants in total population increased between 1960 and 2019, raising fears about economic costs for natives, loss of national identity, and integra- tion issues. In poor countries, international migration raises concerns regarding brain drain of highly-skilled people, as college and university graduates have a higher tendency to emigrate than the less educated (Beine et al., 2021). The accurate understanding and prediction of migration motifs are crucial from a policy- making perspective, to measure for instance the immigration pressure a country faces. Improved models can help to predict and adjust migration flows but also to identify and mitigate adverse effects on all parties involved. The objective of this project is to extend advanced discrete choice model specifications that address issues such as the independence from irrelevant alternatives (IIA). For instance, Beine et al. (2021) implemented a cross-nested logit (CNL) model to approximate the substitution patterns in migration aspirations of Indian individuals more accurately. The specification of such models could be improved by incorporating addi- tional variables related to risk aversion of the individuals, perceived security of their environment, perceived degradation of the climatic conditions or perceived governance in their country could influence aspiration. To address issues related to measurement errors, these variables will be incorporated in the form of latent variables which have been extensively used in the past in the field of discrete choice models. Ultimately, the objective of this project is to extend the use of latent variables in the context of migration aspiration models and examine whether the insights of previous models (such as the CNL), for instance in terms of substitution, hold when improving this specification.

2023

Predicting activity schedules of the population considering non-pharmaceutical interventions, Arthur Nussbaumer

Supervision: Cloe Cortes Balcells, Evangelos Paschalidis, Michel Bierlaire
COVID-19 creates an undeniable bridge between the epidemiological and the transportation community. Due to the lack of a vaccine in the first stage of the pandemic, controlling mobility becomes the key to containing the spreading of the virus. Understanding how and why people perform their daily activities provides crucial information to define more targeted and less disruptive interventions. However, the link between mobility and the spreading of an infectious disease is double-edged: how people move drives the spreading of the virus, and the presence of the virus has clear repercussions on people scheduling their activities. These reschedulings can be due to a self-choice (I no longer go to the gym because I am scared), or because an external imposes it (the government closes the gyms). In this project, we will focus on the latter since we do not own data to calibrate the parameters of self-choice reschedulings. The aim is to predict the activity schedules of the population considering restrictions derived from COVID-19 presence. We will use an agent-based model provided by the TRANSP-OR lab (OASIS) which integrates the different daily scheduling choice dimensions (activity participation, location, schedule, duration, and transportation mode) into a single optimization problem. We hope this approach will provide insight into how the presence of infectious diseases impacts activity-travel behavior and reflect on potential policy implications/interventions.