Back to Search
Start Over
Estimation of minority modes of transportation based on machine learning approach.
- Source :
- Procedia Computer Science; 2022, Vol. 201, p265-272, 8p
- Publication Year :
- 2022
-
Abstract
- The modal choice, the third step of the historical four-stage model (FSM), determines the flows for each mode of transportation. It is generally stimated from the discrete choice models, which require re-building trips conditions for all alternatives, even those marginal. This paper proposes a new alternative based on machine learning methods and resampling techniques to estimate the modal choice by considering the extreme minority mode of transportation, such as intermodal combinations (e.g., car + public transit, bike + public transit). We have proposed two learning algorithms: the decision tree (DT) and the multinomial logistic regression (MNLR). Afer resampling the dataset, the DT presents better predictions (70% of global accuracy and f1-score closes to 70% for all modes of transportation) than the MNLR (35% of global accuracy). [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 18770509
- Volume :
- 201
- Database :
- Supplemental Index
- Journal :
- Procedia Computer Science
- Publication Type :
- Academic Journal
- Accession number :
- 156550696
- Full Text :
- https://doi.org/10.1016/j.procs.2022.03.036