Publications associées à Dynamic Modeling of Trip Completion Rate in Urban Areas with MFD Representations

Publications associées (28)

An improved statistical wake meandering model

Fernando Porté Agel, Corey Dean Markfort, Peter Andreas Brugger

A new statistical wake meandering (SWM) model is proposed that improves on existing models in the literature. Compared to the existing SWM models, the proposed model has a closed description that does not require simulations to create look-up tables while ...

IOP Publishing2024

Land Cover Mapping From Multiple Complementary Experts Under Heavy Class Imbalance

Devis Tuia, Valérie Zermatten, Javiera Francisca Castillo Navarro

Deep learning has emerged as a promising avenue for automatic mapping, demonstrating high efficacy in land cover categorization through various semantic segmentation models. Nonetheless, the practical deployment of these models encounters important challen ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Community-based reconstruction and simulation of a full-scale model of region CA1 of rat hippocampus

Alberto Antonietti, Davide Bella, Pranav Rai, Henry Markram, Felix Schürmann, Eilif Benjamin Muller, Michael Reimann, Marwan Muhammad Ahmed Abdellah, Werner Alfons Hilda Van Geit, Lida Kanari, Srikanth Ramaswamy, James Gonzalo King, Alexis Arnaudon, Ying Shi, András Ecker, Jean-Denis Georges Emile Courcol, Armando Romani, Pramod Shivaji Kumbhar, Judit Planas Carbonell, Michael Emiel Gevaert, Nadir Roman Guerrero, Liesbeth Maria L Vanherpe, Huanxiang Lu, Mohameth François Sy, Cristina Colangelo, Juan Bautista Hernando Vieites, Cyrille Pierre Henri Favreau, Genrich Ivaska, Anna-Kristin Kaufmann, Joni Henrikki Herttuainen, Elvis Boci, Vishal Sood, Thomas Brice Delemontex, Fabien Petitjean, Julian Martin Budd, Mustafa Kerem Kurban

The CA1 region of the hippocampus is one of the most studied regions of the rodent brain, thought to play an important role in cognitive functions such as memory and spatial navigation. Despite a wealth of experimental data on its structure and function, i ...

2024

How Words Move Hearts: Interpretable Machine Learning Models of Bias, Engagement, and Influence in Socio-Political Systems

Aswin Suresh

We study socio-political systems in representative democracies. Motivated by problems that affect the proper functioning of the system, we build computational methods to answer research questions regarding the phenomena occurring in them. For each phenomen ...

EPFL2023

Wind-Topo_Model

Michael Lehning, Jérôme François Sylvain Dujardin

Model and Data: Dujardin, J.; Lehning, M. Wind-Topo_model. EnviDat 2022 ...

Zenodo2022

Large-scale traffic signal control and multimodal network design

Dimitrios Tsitsokas

Traffic congestion constitutes one of the most frequent, yet challenging, problems to address in the urban space. Caused by the concentration of population, whose mobility needs surpass the serving capacity of urban networks, congestion cannot be resolved ...

EPFL2022

Systematic extraction of a control-oriented model from perturbative experiments and SOLPS-ITER for emission front control in TCV

Olivier Claude Martin Février, Artur Perek, Mirko Wensing

Systematic extraction of locally valid dynamic models from experiments is necessary for controller design and the validation of high fidelity models. This paper describes the extraction of a dynamic model in the form of a transfer function, giving the dyna ...

IOP Publishing Ltd2022

An Equivalence Between Data Poisoning and Byzantine Gradient Attacks

Rachid Guerraoui, Le Nguyen Hoang, Sadegh Farhadkhani, Oscar Jean Olivier Villemaud

To study the resilience of distributed learning, the “Byzantine" literature considers a strong threat model where workers can report arbitrary gradients to the parameter server. Whereas this model helped obtain several fundamental results, it has sometimes ...

PMLR2022

Generalization of an Encoder-Decoder LSTM model for flood prediction in ungauged catchments

Olga Fink

Flood prediction in ungauged catchments is usually conducted by hydrological models that are parameterized based on nearby and similar gauged catchments. As an alternative to this process-based modelling, deep learning (DL) models have demonstrated their a ...

ELSEVIER2022

Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies

Michael Reimann, Ran Levi, Christoph Pokorny

In motor-related brain regions, movement intention has been successfully decoded from invivo spike train by isolating a lower-dimension manifold that the high-dimensional spiking activity is constrained to. The mechanism enforcing this constraint remains u ...

PUBLIC LIBRARY SCIENCE2022

Estimating flexibility preferences to resolve temporal scheduling conflicts in activity-based modelling

Michel Bierlaire, Timothy Michael Hillel, Janody Pougala

This paper presents a novel activity-based demand model that combines an optimisation framework for continuous temporal scheduling decisions (i.e. activity timings and durations) with traditional discrete choice models for non-temporal choice dimensions (i ...

SPRINGER2022

Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows

Daniel Keller

Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system work ...

2022

The influence of trip length distribution on urban traffic in network-level models

Mikhail Murashkin

Modeling urban traffic on the level of network is a wide research area oriented to the development of ITS. In this thesis properties of models based on MFD (Macroscopic Fundamental Diagram) are studied. The idea behind MFD is to say that the state of the t ...

EPFL2021

A data-driven perspective on the colours of metal-organic frameworks

Berend Smit, Luc Patiny, Mehrdad Asgari, Seyedmohamad Moosavi, Kevin Maik Jablonka, Christopher Patrick Ireland

Colour is at the core of chemistry and has been fascinating humans since ancient times. It is also a key descriptor of optoelectronic properties of materials and is often used to assess the success of a synthesis. However, predicting the colour of a materi ...

2021

A Modular Workflow for Model Building, Analysis, and Parameter Estimation in Systems Biology and Neuroscience

Daniel Keller

Neuroscience incorporates knowledge from a range of scales, from single molecules to brain wide neural networks. Modeling is a valuable tool in understanding processes at a single scale or the interactions between two adjacent scales and researchers use a ...

HUMANA PRESS INC2022

Characterizing human mobility patterns in rural settings of sub-Saharan Africa

Andrea Rinaldo, Francisco Javier Perez Saez, Theophile Mande

Human mobility is a core component of human behavior and its quantification is critical for understanding its impact on infectious disease transmission, traffic forecasting, access to resources and care, intervention strategies, and migratory flows. When m ...

eLIFE SCIENCES PUBL LTD2021

Modeling, estimation, and control in large-scale urban road networks with remaining travel distance dynamics

Nikolaos Geroliminis, Isik Ilber Sirmatel, Anastasios Kouvelas, Dimitrios Tsitsokas

City-scale control of urban road traffic poses a challenging problem. Dynamical models based on the macroscopic fundamental diagram (MFD) enable development of model predictive perimeter control methods for large-scale urban networks, representing an advan ...

2021

Exploring policy change through agent-based simulation

Raphaël Klein

Policymaking is a complex process that has been studied using policy process theories almost exclusively. These theories have been built using a large number of qualitative cases. Such methods are useful for theory building but remain limited for theory ex ...

EPFL2021

Empirical analysis of large-scale multimodal traffic with multi-sensor data

Nikolaos Geroliminis, Nan Zheng

Recent advances in the network-level traffic flow modelling provide an efficient tool for analyzing traffic performance of large-scale networks. A relationship between density and flow at the network level is developed and widely studied, namely the macros ...

PERGAMON-ELSEVIER SCIENCE LTD2020

A new spatial count data model with Bayesian additive regression trees for accident hot spot identification

Rico Krüger

The identification of accident hot spots is a central task of road safety management. Bayesian count data models have emerged as the workhorse method for producing probabilistic rankings of hazardous sites in road networks. Typically, these methods assume ...

2020