Citation

Chapleau R, Gaudette P, Spurr T. Transp. Res. Rec. 2019; 2673(4): 173-183.

Copyright

(Copyright © 2019, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/0361198119839339

PMID

unavailable

Abstract

Even in a context of rapidly evolving transportation and information technologies, household travel surveys remain an essential source of information for transportation planning. Moreover, as planning authorities become increasingly concerned with reducing the use of the private car, travelers' mode choice patterns should be reexamined. In this study, a machine learning algorithm (Random Forest) was employed to characterize the use of eight different travel modes observed in two consecutive household travel surveys undertaken in Montreal, Canada. The analysis incorporated roughly 160,000 observed trips. The Random Forest algorithm was trained on the 2008 survey data and applied to the 2013 survey. The usefulness of the algorithm was evaluated using two numerical representations: the confusion matrix and the importance matrix. The results of this evaluation showed that the Random Forest algorithm could generate a detailed and precise characterization of travel submarkets for four of the most commonly observed modes of travel (auto-drive, public transit, school bus, and walk) using 11 attributes of households, persons, and trips. However, the auto-passenger mode was difficult to characterize because of its dependence on unobserved intra-household interactions. The algorithm also had difficulty identifying users of rarely observed modes (park-and-ride, kiss-and-ride, bicycle), but performed better in this regard than a traditional mode choice model. Finally, traveler's age and the spatial orientation of origin-destination pairs were found to be decisive factors in the use of the auto-drive mode. This finding, combined with the stability of mode choice patterns observed over 5 years, highlights the difficulty of significantly reducing automobile use.

Language: en