Discrete Choice Analysis: micro-econometrics and machine learning approaches
General aim
Discrete choice analysis (DCA) has become one of the most important frameworks for transportation modelling. Using DCA, the researcher or analyst is able to estimate the influence of all sorts of factors on travel behaviour and demand, and to predict mobility patterns and market shares for transport-related services. Most importantly, this is all done in a quantitative, statistically rigorous way with deep roots in economics and the behavioural sciences. As such, DCA is indispensable for the underpinning of many transport policies and plans.
In this course, we will cover two different perspectives on DCA: the conventional, generally econometrics-based perspective, and a more novel perspective which is gaining ground rapidly, based on recent advances in Machine learning. This combination makes this course unique, compared to other choice modelling courses taught in the Transport community.
This course will contain a mix of theory, implementation guidelines, and hands-on exercises to be completed during the course and under supervision of the lecturers.
More detailed information on the course program in the attached PdF below.
Course leaders: Prof. dr. Stephane Hess and Dr. Sander van Cranenburgh (DUT)
Date: 6 – 9 February 2024
Time: 10:00 – 16:00 h.
Location: TU Delft – faculty TPM
ECTS: 2 ECTS (attendance, including assignments)
Participation: free for TRAIL/Beta/OML/ERIM members and PhD students of participating TRAIL faculties. Others please contact the TRAIL Office info@rstrail.nl.
Registration: please click here.
