TY  - GEN
UR  - https://www.events.tum.de/frontend/index.php?sub=94
A1  - Hillel, Tim
A1  - Bierlaire, Michel
A1  - Elshafie, Mohammed
A1  - Jin, Ying
EP  - 5
AV  - public
N2  - Predicting passenger mode choice is an essential task for transport network simulation and operations
management. Machine Learning (ML) classifiers trained on trip diary data are increasingly being investigated as an alternative to Discrete Choice Models (DCMs) for mode choice prediction.
In order to determine the suitability of a particular algorithm for predicting mode choice, it is crucial
to be able to reliably quantify its predictive performance for unknown trips. In this paper we propose and
test a new framework for performance estimation of classification algorithms.
Firstly, we address three key issues in the literature relating to a) performance metrics for model training, b) sampling methods for model validation, and c) hyperparameter selection for model optimisation.
Alternative methods are proposed for each of these issues to form the new framework. The framework is
then used to compare the suitability of eight different ML classification algorithms for predicting mode
choice. Finally, the implications of the sampling method employed are then investigated experimentally.
ID  - discovery10173006
CY  - Athens, Greece
N1  - This version is the version of record. For information on re-use, please refer to the publisher?s terms and conditions.
PB  - hEART
SP  - 1
TI  - A new framework for assessing classification algorithms for
mode choice prediction
Y1  - 2018/09/07/
T3  - Symposium of the European Association for Research in Transportation
ER  -