IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i19p8454-d1488154.html
   My bibliography  Save this article

Decoding Jakarta Women’s Non-Working Travel-Mode Choice: Insights from Interpretable Machine-Learning Models

Author

Listed:
  • Roosmayri Lovina Hermaputi

    (College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China)

  • Chen Hua

    (College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
    Center for Balance Architecture, Zhejiang University, Hangzhou 310058, China)

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.

Suggested Citation

  • Roosmayri Lovina Hermaputi & Chen Hua, 2024. "Decoding Jakarta Women’s Non-Working Travel-Mode Choice: Insights from Interpretable Machine-Learning Models," Sustainability, MDPI, vol. 16(19), pages 1-39, September.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:19:p:8454-:d:1488154
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/19/8454/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/19/8454/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rahul Goel & Anna Goodman & Rachel Aldred & Ryota Nakamura & Lambed Tatah & Leandro Martin Totaro Garcia & Belen Zapata-Diomedi & Thiago Herick de Sa & Geetam Tiwari & Audrey de Nazelle & Marko Tainio, 2022. "Cycling behaviour in 17 countries across 6 continents: levels of cycling, who cycles, for what purpose, and how far?," Transport Reviews, Taylor & Francis Journals, vol. 42(1), pages 58-81, January.
    2. Muhammad Adeel & Anthony G. O. Yeh & Feng Zhang, 2017. "Gender inequality in mobility and mode choice in Pakistan," Transportation, Springer, vol. 44(6), pages 1519-1534, November.
    3. Murray, Scott D. & Jin, Hyun Seung & Martin, Brett A.S., 2022. "The role of shopping orientation in variety-seeking behaviour," Journal of Business Research, Elsevier, vol. 145(C), pages 188-197.
    4. Prati, Gabriele & Fraboni, Federico & De Angelis, Marco & Pietrantoni, Luca & Johnson, Daniel & Shires, Jeremy, 2019. "Gender differences in cycling patterns and attitudes towards cycling in a sample of European regular cyclists," Journal of Transport Geography, Elsevier, vol. 78(C), pages 1-7.
    5. Djoen San Santoso & Koji Tsunokawa, 2005. "Spatial Transferability and Updating Analysis of Mode Choice Models in Developing Countries," Transportation Planning and Technology, Taylor & Francis Journals, vol. 28(5), pages 341-358, July.
    6. Gil Solá, Ana & Vilhelmson, Bertil & Larsson, Anders, 2018. "Understanding sustainable accessibility in urban planning: Themes of consensus, themes of tension," Journal of Transport Geography, Elsevier, vol. 70(C), pages 1-10.
    7. Lo, A. W.-T. & Houston, D., 2018. "How do compact, accessible, and walkable communities promote gender equality in spatial behavior?," Journal of Transport Geography, Elsevier, vol. 68(C), pages 42-54.
    8. Ghadir Pourhashem & Eva Malichová & Terezia Piscová & Tatiana Kováčiková, 2022. "Gender Difference in Perception of Value of Travel Time and Travel Mode Choice Behavior in Eight European Countries," Sustainability, MDPI, vol. 14(16), pages 1-28, August.
    9. Bhat, Chandra R. & Srinivasan, Sivaramakrishnan, 2005. "A multidimensional mixed ordered-response model for analyzing weekend activity participation," Transportation Research Part B: Methodological, Elsevier, vol. 39(3), pages 255-278, March.
    10. Isti Hidayati & Claudia Yamu & Wendy Tan, 2019. "The Emergence of Mobility Inequality in Greater Jakarta, Indonesia: A Socio-Spatial Analysis of Path Dependencies in Transport–Land Use Policies," Sustainability, MDPI, vol. 11(18), pages 1-18, September.
    11. Isti Hidayati & C. Yamu & W. Tan, 2021. "Realised pedestrian accessibility of an informal settlement in Jakarta, Indonesia," Journal of Urbanism: International Research on Placemaking and Urban Sustainability, Taylor & Francis Journals, vol. 14(4), pages 434-456, October.
    12. Muhammad Zudhy Irawan & Prawira Fajarindra Belgiawan & Ari Krisna Mawira Tarigan & Fajar Wijanarko, 2020. "To compete or not compete: exploring the relationships between motorcycle-based ride-sourcing, motorcycle taxis, and public transport in the Jakarta metropolitan area," Transportation, Springer, vol. 47(5), pages 2367-2389, October.
    13. Anil NP Koushik & M. Manoj & N. Nezamuddin, 2020. "Machine learning applications in activity-travel behaviour research: a review," Transport Reviews, Taylor & Francis Journals, vol. 40(3), pages 288-311, May.
    14. Hui Zhang & Li Zhang & Yanjun Liu & Lele Zhang, 2023. "Understanding Travel Mode Choice Behavior: Influencing Factors Analysis and Prediction with Machine Learning Method," Sustainability, MDPI, vol. 15(14), pages 1-20, July.
    15. Weijia (Vivian) Li & Kara M. Kockelman, 2022. "How does machine learning compare to conventional econometrics for transport data sets? A test of ML versus MLE," Growth and Change, Wiley Blackwell, vol. 53(1), pages 342-376, March.
    16. Tri Basuki Joewono & Mohamed Yusuf Faridian Wirayat & Prawira Fajarindra Belgiawan & I Gusti Ayu Andani & Clint Gunawijaya, 2023. "Users’ Preferences in Selecting Transportation Modes for Leisure Trips in the Digital Era: Evidence from Bandung, Indonesia," Sustainability, MDPI, vol. 15(3), pages 1-24, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gil Solá, Ana & Vilhelmson, Bertil, 2022. "To choose, or not to choose, a nearby activity option: Understanding the gendered role of proximity in urban settings," Journal of Transport Geography, Elsevier, vol. 99(C).
    2. Lovejoy, Kristin, 2012. "Mobility Fulfillment Among Low-car Households: Implications for Reducing Auto Dependence in the United States," Institute of Transportation Studies, Working Paper Series qt4v44b5qn, Institute of Transportation Studies, UC Davis.
    3. Weiss, Adam & Habib, Khandker Nurul, 2017. "Examining the difference between park and ride and kiss and ride station choices using a spatially weighted error correlation (SWEC) discrete choice model," Journal of Transport Geography, Elsevier, vol. 59(C), pages 111-119.
    4. Svavarsdottir, Gudrun & Clark, Andrew E. & Stefansson, Gunnar & Asgeirsdottir, Tinna Laufey, 2024. "Where does money matter more?," Journal of Economic Behavior & Organization, Elsevier, vol. 221(C), pages 350-365.
    5. Muhammad Ahmad Al-Rashid & Muhammad Nadeem & Tiziana Campisi & Iftikhar Ahmad, 2023. "Exploring the Role of Socio-Demographic Characteristics on Gendered Social Exclusion: Empirical Evidence from Older Adults in Pakistan," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 169(3), pages 847-862, October.
    6. Tomasz Bieliński & Łukasz Dopierała & Maciej Tarkowski & Agnieszka Ważna, 2020. "Lessons from Implementing a Metropolitan Electric Bike Sharing System," Energies, MDPI, vol. 13(23), pages 1-21, November.
    7. Castro, Marisol & Bhat, Chandra R. & Pendyala, Ram M. & Jara-Díaz, Sergio R., 2012. "Accommodating multiple constraints in the multiple discrete–continuous extreme value (MDCEV) choice model," Transportation Research Part B: Methodological, Elsevier, vol. 46(6), pages 729-743.
    8. Timmons, Shane & Andersson, Ylva & McGowan, Féidhlim & Lunn, Pete, 2023. "Using behavioural science to design and implement active travel infrastructure: A narrative review of evidence," Papers WP745, Economic and Social Research Institute (ESRI).
    9. Allahviranloo, Mahdieh & Recker, Will, 2013. "Daily activity pattern recognition by using support vector machines with multiple classes," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 16-43.
    10. Noah Kaiser & Christina K. Barstow, 2022. "Rural Transportation Infrastructure in Low- and Middle-Income Countries: A Review of Impacts, Implications, and Interventions," Sustainability, MDPI, vol. 14(4), pages 1-48, February.
    11. Ana Gil Solá & Bertil Vilhelmson, 2018. "Negotiating Proximity in Sustainable Urban Planning: A Swedish Case," Sustainability, MDPI, vol. 11(1), pages 1-18, December.
    12. Yue Liu & Jun Chen & Weiguang Wu & Jiao Ye, 2019. "Typical Combined Travel Mode Choice Utility Model in Multimodal Transportation Network," Sustainability, MDPI, vol. 11(2), pages 1-15, January.
    13. Kirtonia, Sajeeb & Sun, Yanshuo, 2022. "Evaluating rail transit's comparative advantages in travel cost and time over taxi with open data in two U.S. cities," Transport Policy, Elsevier, vol. 115(C), pages 75-87.
    14. Kaplan, Sigal & Shiftan, Yoram & Bekhor, Shlomo, 2012. "Development and estimation of a semi-compensatory model with a flexible error structure," Transportation Research Part B: Methodological, Elsevier, vol. 46(2), pages 291-304.
    15. Rupi, Federico & Freo, Marzia & Poliziani, Cristian & Postorino, Maria Nadia & Schweizer, Joerg, 2023. "Analysis of gender-specific bicycle route choices using revealed preference surveys based on GPS traces," Transport Policy, Elsevier, vol. 133(C), pages 1-14.
    16. Li, Zhitao & Tang, Jinjun & Zhao, Chuyun & Gao, Fan, 2023. "Improved centrality measure based on the adapted PageRank algorithm for urban transportation multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    17. Li, Mengya & Kwan, Mei-Po & Hu, Wenyan & Li, Rui & Wang, Jun, 2023. "Examining the effects of station-level factors on metro ridership using multiscale geographically weighted regression," Journal of Transport Geography, Elsevier, vol. 113(C).
    18. Zhang, Yunen & Shao, Wei & Quach, Sara & Thaichon, Park & Li, Qianmin, 2024. "Examining the moderating effects of shopping orientation, product knowledge and involvement on the effectiveness of Virtual Reality (VR) retail environment," Journal of Retailing and Consumer Services, Elsevier, vol. 78(C).
    19. Jaeyoung Lee & Farrukh Baig & Mir Aftab Hussain Talpur & Sajan Shaikh, 2021. "Public Intentions to Purchase Electric Vehicles in Pakistan," Sustainability, MDPI, vol. 13(10), pages 1-18, May.
    20. Echeverría, Lucía & Giménez-Nadal, J. Ignacio & Alberto Molina, José, 2022. "Who uses green mobility? Exploring profiles in developed countries," Transportation Research Part A: Policy and Practice, Elsevier, vol. 163(C), pages 247-265.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:19:p:8454-:d:1488154. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.