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TRIMIS

Econometric Machine Learning for better Heterogeneity Representation

PROJECTS
Funding
European
European Union
Duration
-
Geo-spatial type
Other
Total project cost
€0
EU Contribution
€214 934
Project Acronym
Econ-ML
STRIA Roadmaps
Smart mobility and services (SMO)
Transport mode
Road icon
Transport policies
Societal/Economic issues
Transport sectors
Passenger transport,
Micromobility,
Active mobility

Overview

Call for proposal
HORIZON-MSCA-2021-PF-01
Link to CORDIS
Background & Policy context

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. 

Objectives

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 McFadden’s 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.

Funding

Specific funding programme
HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
Other Programme
HORIZON-MSCA-2021-PF-01-01 MSCA Postdoctoral Fellowships 2021

Partners

Lead Organisation
Organisation
DANMARKS TEKNISKE UNIVERSITET
Address
ANKER ENGELUNDS VEJ 101, 2800 KONGENS LYNGBY, Denmark
Organisation website
EU Contribution
€214 934

Technologies

Technology Theme
Cycling measures
Technology
Multi-dimensional, spatially differentiated model of bicycle mobility
Development phase
Demonstration/prototyping/Pilot Production

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