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Title:

Interpretable Machine Learning for Mode Choice Modeling on Tracking-Based Revealed Preference Data

Document type:
Zeitschriftenaufsatz
Author(s):
Dahmen, Victoria; Weikl, Simone; Bogenberger, Klaus
Abstract:
Mode choice modeling is imperative for predicting and understanding travel behavior. For this purpose, machine learning (ML) models have increasingly been applied to stated preference and traditional self-recorded revealed preference data with promising results, particularly for extreme gradient boosting (XGBoost) and random forest (RF) models. Because of the rise in the use of tracking-based smartphone applications for recording travel behavior, we address the important and unprecedented task o...     »
Keywords:
mode choice, interpretable machine learning, sensitivity analysis, revealed preference, smartphone tracking, travel behavior
Dewey Decimal Classification:
500 Naturwissenschaften; 600 Technik; 620 Ingenieurwissenschaften
Journal title:
Transportation Research Record: Journal of the Transportation Research Board
Year:
2024
Fulltext / DOI:
doi:10.1177/03611981241246973
Publisher:
SAGE Publications
E-ISSN:
2169-4052
Status:
Verlagsversion / published
Date of publication:
23.05.2024
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