Discrete Choice Analysis: micro-econometrics and machine learning approaches
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 choice behavior, and to predict mobility patterns and market shares for transport-related services; all this, in a quantitative, statistically rigorous way with deep roots in economics and the behavioral sciences. As such, DCA is indispensable for the underpinning of many transport policies and plans.
In this course, two different perspectives on DCA will be covered: the conventional, econometrics-based perspective (also called Discrete choice theory); and a more novel perspective which is gaining ground rapidly, based on recent advances in Machine learning (more specifically, Artificial neural networks). 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 lecturer.
⇒ More detailed information on the course in the attached program below.
Lecturers: Prof. Caspar Chorus & Dr. Sander van Cranenburgh
Dates: 6, 7 & 8 April 2020
Time: 09.30 – 17.00 h
Location: TU Delft
ECTS: 2 (attendance + assignments)
Participation: free for TRAIL/Beta/OML members and PhD students of participating faculties