IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i17p7802-d1473323.html
   My bibliography  Save this article

Model of Sustainable Household Mobility in Multi-Modal Transportation Networks

Author

Listed:
  • Igor Kabashkin

    (Engineering Faculty, Transport and Telecommunication Institute, Lauvas 2, LV-1019 Riga, Latvia)

Abstract

Nowadays, urban and suburban areas face increasing environmental pressures, and encouraging sustainable transportation behaviors at the household level has become crucial. This paper presents a model of a decision support system (DSS) for promoting sustainable household mobility choices in multi-modal transport networks. The system was modeled using an enhanced Petri Net approach, allowing for the dynamic representation of complex transport networks and multi-modal journey options. The model incorporated various sustainability factors. These were combined into a single environmental impact score, which was considered alongside travel time and cost in the route optimization process. Simulation results demonstrated the DSS’s capability to guide users toward more sustainable mobility choices. The model also showed potential as a tool for policymakers to assess the impact of various sustainable transportation initiatives and infrastructure investments. This paper discussed the versatile applications of the system. It also addressed the limitations of Petri Net models in transportation systems and suggested future research directions.

Suggested Citation

  • Igor Kabashkin, 2024. "Model of Sustainable Household Mobility in Multi-Modal Transportation Networks," Sustainability, MDPI, vol. 16(17), pages 1-21, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:17:p:7802-:d:1473323
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/17/7802/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/17/7802/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Soora Rasouli & Harry Timmermans, 2014. "Activity-based models of travel demand: promises, progress and prospects," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 18(1), pages 31-60, March.
    2. 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.
    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. Mohammad Hesam Hafezi & Lei Liu & Hugh Millward, 2019. "A time-use activity-pattern recognition model for activity-based travel demand modeling," Transportation, Springer, vol. 46(4), pages 1369-1394, August.
    2. Kevin Credit & Zander Arnao, 2023. "A method to derive small area estimates of linked commuting trips by mode from open source LODES and ACS data," Environment and Planning B, , vol. 50(3), pages 709-722, March.
    3. Wang, Yihong & Correia, Gonçalo Homem de Almeida & de Romph, Erik & Timmermans, H.J.P., 2017. "Using metro smart card data to model location choice of after-work activities: An application to Shanghai," Journal of Transport Geography, Elsevier, vol. 63(C), pages 40-47.
    4. Blom Västberg, Oskar & Karlström, Anders & Jonsson, Daniel & Sundberg, Marcus, 2016. "Including time in a travel demand model using dynamic discrete choice," MPRA Paper 75336, University Library of Munich, Germany, revised 11 Nov 2016.
    5. Paul Baustert & Tomás Navarrete Gutiérrez & Thomas Gibon & Laurent Chion & Tai-Yu Ma & Gabriel Leite Mariante & Sylvain Klein & Philippe Gerber & Enrico Benetto, 2019. "Coupling Activity-Based Modeling and Life Cycle Assessment—A Proof-of-Concept Study on Cross-Border Commuting in Luxembourg," Sustainability, MDPI, vol. 11(15), pages 1-24, July.
    6. Mahdieh Allahviranloo & Thomas Bonet & Jérémy Diez, 2021. "Introducing shared life experience metric in urban planning," Transportation, Springer, vol. 48(3), pages 1125-1148, June.
    7. Yifei Xie & Mazen Danaf & Carlos Lima Azevedo & Arun Prakash Akkinepally & Bilge Atasoy & Kyungsoo Jeong & Ravi Seshadri & Moshe Ben-Akiva, 2019. "Behavioral modeling of on-demand mobility services: general framework and application to sustainable travel incentives," Transportation, Springer, vol. 46(6), pages 2017-2039, December.
    8. Kai Shen & Jan-Dirk Schmöcker & Wenzhe Sun & Ali Gul Qureshi, 2023. "Calibration of sightseeing tour choices considering multiple decision criteria with diminishing reward," Transportation, Springer, vol. 50(5), pages 1897-1921, October.
    9. Jahun Koo & Jiyoon Kim & Sungtaek Choi & Sangho Choo, 2022. "Identifying the Causal Relationship between Travel and Activity Times: A Structural Equation Modeling Approach," Sustainability, MDPI, vol. 14(8), pages 1-18, April.
    10. Mahdieh Allahviranloo & Will Recker, 2015. "Mining activity pattern trajectories and allocating activities in the network," Transportation, Springer, vol. 42(4), pages 561-579, July.
    11. Fu, Xiao & Wu, Youqi & Huang, Di & Wu, Jianjun, 2022. "An activity-based model for transit network design and activity location planning in a three-party game framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    12. Siyu Li & Der-Horng Lee, 2017. "Learning daily activity patterns with probabilistic grammars," Transportation, Springer, vol. 44(1), pages 49-68, January.
    13. Oskar Blom Västberg & Anders Karlström & Daniel Jonsson & Marcus Sundberg, 2020. "A Dynamic Discrete Choice Activity-Based Travel Demand Model," Transportation Science, INFORMS, vol. 54(1), pages 21-41, January.
    14. Shenhao Wang & Baichuan Mo & Stephane Hess & Jinhua Zhao, 2021. "Comparing hundreds of machine learning classifiers and discrete choice models in predicting travel behavior: an empirical benchmark," Papers 2102.01130, arXiv.org.
    15. Lídia Montero & Lucía Mejía-Dorantes & Jaume Barceló, 2024. "Land Use, Travel Patterns and Gender in Barcelona: A Sequence Analysis Approach," Sustainability, MDPI, vol. 16(20), pages 1-26, October.
    16. Ha, Tran Vinh & Asada, Takumi & Arimura, Mikiharu, 2019. "Determination of the influence factors on household vehicle ownership patterns in Phnom Penh using statistical and machine learning methods," Journal of Transport Geography, Elsevier, vol. 78(C), pages 70-86.
    17. Allahviranloo, Mahdieh & Aissaoui, Leila, 2019. "A comparison of time-use behavior in metropolitan areas using pattern recognition techniques," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 271-287.
    18. Peter Widhalm & Yingxiang Yang & Michael Ulm & Shounak Athavale & Marta González, 2015. "Discovering urban activity patterns in cell phone data," Transportation, Springer, vol. 42(4), pages 597-623, July.
    19. Kim, Eui-Jin & Kim, Youngseo & Jang, Sunghoon & Kim, Dong-Kyu, 2021. "Tourists’ preference on the combination of travel modes under Mobility-as-a-Service environment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 236-255.
    20. Liu, Peng & Liao, Feixiong & Huang, Hai-Jun & Timmermans, Harry, 2015. "Dynamic activity-travel assignment in multi-state supernetworks," Transportation Research Part B: Methodological, Elsevier, vol. 81(P3), pages 656-671.

    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:17:p:7802-:d:1473323. 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.