Skip to main content
Ir a la página de inicio de la Comisión Europea (se abrirá en una nueva ventana)
español es
CORDIS - Resultados de investigaciones de la UE
CORDIS

Econometric Machine Learning for better Heterogeneity Representation

Objetivo

Modeling behavioral patterns of commuters and their decision-making process is crucial to develop sustainable and effective transport policies, predict and forecast the travel mode choices of a certain population with respect to changes in some attributes or components of the transportation system, and determine the different sources of heterogeneity in tastes and preferences. Econ-ML is about developing hybrid frameworks that combine several machine learning techniques with econometric discrete choice models to better account for different aspects of unobserved heterogeneity within a population such as systematic and random taste variations in addition to market segmentation. The proposed models would abide by McFaddens vision of an appropriate econometric choice model in order to maintain the behavioral interpretability while improving the prediction and forecasting capabilities. Moreover, this project will focus on estimating the proposed models using Bayesian Variational Inference (VI) techniques and on providing solutions to overcome the corresponding limitations of such methods. In addition, a comparison of traditional estimation techniques such as Maximum Simulated Likelihood Estimation (MSLE) and Expectation-Maximization (EM) with Bayesian Variational Inference techniques will be conducted, with the aim of providing recommendations on when each estimation method should be used. The ultimate goal is to apply the proposed framework to real-world case studies (e.g. shared mobility, biking behavior in Copenhagen, adoption of electric vehicles, etc.) and provide the authorities and operators with forecasts and recommendations for new policies that might mitigate the negative impacts of the transportation system.

Ámbito científico (EuroSciVoc)

Este proyecto aún no se ha clasificado con EuroSciVoc.
Sugiera los ámbitos científicos que considere más relevantes y ayúdenos a mejorar nuestro servicio de clasificación.

Palabras clave

Programa(s)

Tema(s)

Régimen de financiación

HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

Ver todos los proyectos financiados en el marco de este régimen de financiación

Convocatoria de propuestas

(se abrirá en una nueva ventana) HORIZON-MSCA-2021-PF-01

Ver todos los proyectos financiados en el marco de esta convocatoria

Coordinador

DANMARKS TEKNISKE UNIVERSITET
Aportación neta de la UEn
€ 214 934,40
Dirección
ANKER ENGELUNDS VEJ 101
2800 Kongens Lyngby
Dinamarca
Región
Danmark Hovedstaden Københavns omegn
Tipo de actividad
Higher or Secondary Education Establishments
Enlaces
Coste total
Sin datos

Socios (1)