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Titel:

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

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Poster
Autor(en):
Dahmen, Victoria; Weikl, Simone; Bogenberger, Klaus
Abstract:
Mode choice modeling is imperative for both 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 XGBoost and Random Forest (RF) models. Due to the rise in the use of tracking-based smartphone applications for recording travel behavior, we here address the important task of testing these ML models for mode choic...     »
Stichworte:
mode choice; interpretable machine learning; revealed preference; smartphone-tracking; travel behavior
Dewey-Dezimalklassifikation:
000 Informatik, Wissen, Systeme; 620 Ingenieurwissenschaften
Kongress- / Buchtitel:
Transportation Research Board Annual Meeting
Publikationsdatum:
09.10.2024
Jahr:
2024
Monat:
Jan
Reviewed:
ja
Sprache:
en
Volltext / DOI:
doi:10.13140/RG.2.2.33088.92166/1
TUM Einrichtung:
Lehrstuhl für Verkehrstechnik
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