[HTML][HTML] A Machine Learning Comparison of Transportation Mode Changes from High-Speed Railway Promotion in Thailand

C Banyong, N Hantanong, P Wisutwattanasak… - Results in …, 2024 - Elsevier
Thailand's collaboration with China to develop High-Speed Rail (HSR) represents a crucial
step in enhancing transportation infrastructure and promoting regional economic growth …

[PDF][PDF] Decoding Jakarta Women's Non-Working Travel-Mode Choice: Insights from Interpretable Machine-Learning Models

RL Hermaputi, C Hua - Sustainability, 2024 - researchgate.net
Using survey data from three dwelling types in Jakarta, we examine how dwelling type,
socioeconomic identity, and commuting distance affect women's travel-mode choices and …

RUMBoost: Gradient Boosted Random Utility Models

N Salvadé, T Hillel - arXiv preprint arXiv:2401.11954, 2024 - arxiv.org
This paper introduces the RUMBoost model, a novel discrete choice modelling approach
that combines the interpretability and behavioural robustness of Random Utility Models …

[PDF][PDF] Analyzing the 9-Euro-Ticket Mode Choice Impact Using GPS Panel Data and Discrete Choice Models: First Insights

F Beck, SÁO Martínez, K Bogenberger, A Loder - 2024 - mediatum.ub.tum.de
Estimating behavioral parameters for mode choice typically relies on revealed or stated
preference data. However, applying GPS-based revealed preference (GPS-RP) panel data …

[PDF][PDF] The Mobilität. Leben Study: A 20-Month Mobility-Tracking Panel

A Loder, V Dahmen, I Waldorf… - Arbeitsberichte …, 2024 - mediatum.ub.tum.de
Abstract The “Mobilität. Leben” study is a twenty-month panel study with a six-wave survey
and semipassive travel diaries with waypoint tracking using a smartphone app that was …

[PDF][PDF] Dr. Ana Tsui Moreno*, Santiago Alvarez-Ossorio2, Dr. Rolf Moeckel3, and Dr.

K Bogenberger - transp-or.epfl.ch
This study uses one year of continuous tracking data to explore the stability of individuals'
travel behavior over extended periods. The primary objective is to develop a robust …