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Open AccessArticle
Decoding Jakarta Women’s Non-Working Travel-Mode Choice: Insights from Interpretable Machine-Learning Models
by
Roosmayri Lovina Hermaputi
Roosmayri Lovina Hermaputi 1,* and
Chen Hua
Chen Hua 1,2
1
College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
2
Center for Balance Architecture, Zhejiang University, Hangzhou 310058, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(19), 8454; https://doi.org/10.3390/su16198454 (registering DOI)
Submission received: 24 July 2024
/
Revised: 16 September 2024
/
Accepted: 25 September 2024
/
Published: 28 September 2024
Abstract
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 motivations behind women’s choices for nearby and distant non-working trips. We compared the performance of the multinomial logit (MNL) model with two machine-learning classifiers, random forest (RF) and XGBoost, using Shapley additive explanations (SHAP) for interpretation. The models’ efficacy varies across different datasets, with XGBoost mostly outperforming other models. The women’s preferred commuting modes varied by dwelling type and trip purpose, but their motives for choosing the nearest activity were similar. Over half of the women rely on private motorized vehicles, with women living in the gated community heavily relying on private cars. For nearby shopping trips, low income and young age discourage women in urban villages (kampungs) and apartment complexes from walking. Women living in gated communities often choose private cars to fulfill household responsibilities, enabling them to access distant options. For nearby leisure, longer commutes discourage walking except for residents of apartment complexes. Car ownership and household responsibilities increase private car use for distant options. SHAP analysis offers practitioners insights into identifying key variables affecting travel-mode choice to design effective targeted interventions that address women’s mobility needs.
Share and Cite
MDPI and ACS Style
Hermaputi, R.L.; Hua, C.
Decoding Jakarta Women’s Non-Working Travel-Mode Choice: Insights from Interpretable Machine-Learning Models. Sustainability 2024, 16, 8454.
https://doi.org/10.3390/su16198454
AMA Style
Hermaputi RL, Hua C.
Decoding Jakarta Women’s Non-Working Travel-Mode Choice: Insights from Interpretable Machine-Learning Models. Sustainability. 2024; 16(19):8454.
https://doi.org/10.3390/su16198454
Chicago/Turabian Style
Hermaputi, Roosmayri Lovina, and Chen Hua.
2024. "Decoding Jakarta Women’s Non-Working Travel-Mode Choice: Insights from Interpretable Machine-Learning Models" Sustainability 16, no. 19: 8454.
https://doi.org/10.3390/su16198454
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