IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i16p6422-d396834.html
   My bibliography  Save this article

Trip Chaining Model with Classification and Optimization Parameters

Author

Listed:
  • Domokos Esztergár-Kiss

    (Department of Transport Technology and Economics, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics (BME), 1111 Budapest, Hungary)

Abstract

In order to model the complex requirements of users travelling in an urban environment, the relevant parameters for creating activity chains have to be identified. In this study, travel related parameters were collected and grouped into two main types: classification parameters and optimization parameters. In the case of optimization parameters, further grouping was performed where general and comfort parameters were introduced. Additionally, the possible values and data sources of the parameters were identified. A utility function was created to take into account the optimization parameters and the weights. Weights related to comfort optimization parameters were aggregated to decrease the number of required settings by the users. Finally, the features of the proposed optimization algorithm are described. With the identified parameters, aggregated weights and elaborated utility function activity chains can be optimized for users with different requirements.

Suggested Citation

  • Domokos Esztergár-Kiss, 2020. "Trip Chaining Model with Classification and Optimization Parameters," Sustainability, MDPI, vol. 12(16), pages 1-15, August.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:16:p:6422-:d:396834
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/16/6422/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/16/6422/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chieh-Hua Wen & Frank Koppelman, 2000. "A conceptual and methdological framework for the generation of activity-travel patterns," Transportation, Springer, vol. 27(1), pages 5-23, February.
    2. Md. Tazul Islam & Khandker M. Nurul Habib, 2012. "Unraveling the relationship between trip chaining and mode choice: evidence from a multi-week travel diary," Transportation Planning and Technology, Taylor & Francis Journals, vol. 35(4), pages 409-426, January.
    3. Pronello, Cristina & Camusso, Cristian, 2011. "Travellers’ profiles definition using statistical multivariate analysis of attitudinal variables," Journal of Transport Geography, Elsevier, vol. 19(6), pages 1294-1308.
    4. Gianpaolo Ghiani & Emanuele Manni & Barrett W. Thomas, 2012. "A Comparison of Anticipatory Algorithms for the Dynamic and Stochastic Traveling Salesman Problem," Transportation Science, INFORMS, vol. 46(3), pages 374-387, August.
    5. Subbarao, S.S.V. & Krishna Rao, K,V., 2013. "Trip Chaining Behavior in Developing Countries: A Study of Mumbai Metropolitan Region, India," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 53, pages 1-7.
    6. Bowman, J. L. & Ben-Akiva, M. E., 2001. "Activity-based disaggregate travel demand model system with activity schedules," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(1), pages 1-28, January.
    7. Cristina Pronello & Cristian Camusso, 2011. "Travellers’ profiles definition using statistical multivariate analysis of attitudinal variables," Post-Print halshs-01084027, HAL.
    8. Bhat, Chandra R. & Singh, Sujit K., 2000. "A comprehensive daily activity-travel generation model system for workers," Transportation Research Part A: Policy and Practice, Elsevier, vol. 34(1), pages 1-22, January.
    9. David Charypar & Kai Nagel, 2005. "Generating complete all-day activity plans with genetic algorithms," Transportation, Springer, vol. 32(4), pages 369-397, July.
    10. Linda Nijland & Theo Arentze & Harry Timmermans, 2012. "Incorporating planned activities and events in a dynamic multi-day activity agenda generator," Transportation, Springer, vol. 39(4), pages 791-806, July.
    11. Ron Buliung & Matthew Roorda & Tarmo Remmel, 2008. "Exploring spatial variety in patterns of activity-travel behaviour: initial results from the Toronto Travel-Activity Panel Survey (TTAPS)," Transportation, Springer, vol. 35(6), pages 697-722, November.
    12. Sehrish Malik & DoHyeun Kim, 2019. "Optimal Travel Route Recommendation Mechanism Based on Neural Networks and Particle Swarm Optimization for Efficient Tourism Using Tourist Vehicular Data," Sustainability, MDPI, vol. 11(12), pages 1-26, June.
    13. Prillwitz, Jan & Barr, Stewart, 2011. "Moving towards sustainability? Mobility styles, attitudes and individual travel behaviour," Journal of Transport Geography, Elsevier, vol. 19(6), pages 1590-1600.
    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. Thomas Klinger & Martin Lanzendorf, 2016. "Moving between mobility cultures: what affects the travel behavior of new residents?," Transportation, Springer, vol. 43(2), pages 243-271, March.
    2. Xuemei Fu, 2021. "A novel perspective to enhance the role of TPB in predicting green travel: the moderation of affective-cognitive congruence of attitudes," Transportation, Springer, vol. 48(6), pages 3013-3035, December.
    3. Wolf, Ingo & Schröder, Tobias, 2019. "Connotative meanings of sustainable mobility: A segmentation approach using cultural sentiments," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 259-280.
    4. Haustein, Sonja & Nielsen, Thomas A. Sick, 2016. "European mobility cultures: A survey-based cluster analysis across 28 European countries," Journal of Transport Geography, Elsevier, vol. 54(C), pages 173-180.
    5. Lijun Sun & Xinyu Chen & Zhaocheng He & Luis F. Miranda-Moreno, 2023. "Routine Pattern Discovery and Anomaly Detection in Individual Travel Behavior," Networks and Spatial Economics, Springer, vol. 23(2), pages 407-428, June.
    6. Joseph F. Wyer, 2018. "Urban Transportation Mode Choice And Trip Complexity: Bicyclists Stick To Their Gears," Economic Inquiry, Western Economic Association International, vol. 56(3), pages 1777-1787, July.
    7. Alonso-González, María J. & Hoogendoorn-Lanser, Sascha & van Oort, Niels & Cats, Oded & Hoogendoorn, Serge, 2020. "Drivers and barriers in adopting Mobility as a Service (MaaS) – A latent class cluster analysis of attitudes," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 378-401.
    8. Abdul Rawoof Pinjari & Chandra R. Bhat, 2011. "Activity-based Travel Demand Analysis," Chapters, in: André de Palma & Robin Lindsey & Emile Quinet & Roger Vickerman (ed.), A Handbook of Transport Economics, chapter 10, Edward Elgar Publishing.
    9. Yang, Liya & Shen, Qing & Li, Zhibin, 2016. "Comparing travel mode and trip chain choices between holidays and weekdays," Transportation Research Part A: Policy and Practice, Elsevier, vol. 91(C), pages 273-285.
    10. Grischkat, Sylvie & Hunecke, Marcel & Böhler, Susanne & Haustein, Sonja, 2014. "Potential for the reduction of greenhouse gas emissions through the use of mobility services," Transport Policy, Elsevier, vol. 35(C), pages 295-303.
    11. Ghadir Pourhashem & Christina Georgouli & Eva Malichová & Milan Straka & Tatiana Kováčiková, 2024. "Factors influencing the perceived value of travel time in European urban areas," Transportation, Springer, vol. 51(4), pages 1525-1545, August.
    12. Scott, Darren M. & Kanaroglou, Pavlos S., 2002. "An activity-episode generation model that captures interactions between household heads: development and empirical analysis," Transportation Research Part B: Methodological, Elsevier, vol. 36(10), pages 875-896, December.
    13. Chankrajang, Thanyaporn & Muttarak, Raya, 2017. "Green Returns to Education: Does Schooling Contribute to Pro-Environmental Behaviours? Evidence from Thailand," Ecological Economics, Elsevier, vol. 131(C), pages 434-448.
    14. Ren, Xiyuan & Chow, Joseph Y.J., 2022. "A random-utility-consistent machine learning method to estimate agents’ joint activity scheduling choice from a ubiquitous data set," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 396-418.
    15. Liu, Peng & Liao, Feixiong & Tian, Qiong & Huang, Hai-Jun & Timmermans, Harry, 2020. "Day-to-day needs-based activity-travel dynamics and equilibria in multi-state supernetworks," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 208-227.
    16. Xuemei Fu & Zhicai Juan, 2016. "Empirical analysis and comparisons about time-allocation patterns across segments based on mode-specific preferences," Transportation, Springer, vol. 43(1), pages 37-51, January.
    17. Cristina Pronello & Jean-Baptiste Gaborieau, 2018. "Engaging in Pro-Environment Travel Behaviour Research from a Psycho-Social Perspective: A Review of Behavioural Variables and Theories," Sustainability, MDPI, vol. 10(7), pages 1-22, July.
    18. Badiola, Nicolás & Raveau, Sebastián & Galilea, Patricia, 2019. "Modelling preferences towards activities and their effect on departure time choices," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 39-51.
    19. Jia, Ning & Li, Liying & Ling, Shuai & Ma, Shoufeng & Yao, Wang, 2018. "Influence of attitudinal and low-carbon factors on behavioral intention of commuting mode choice – A cross-city study in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 111(C), pages 108-118.
    20. Tri Basuki Joewono & Ari K. M. Tarigan & Muhamad Rizki, 2019. "Segmentation, Classification, and Determinants of In-Store Shopping Activity and Travel Behaviour in the Digitalisation Era: The Context of a Developing Country," Sustainability, MDPI, vol. 11(6), pages 1-23, March.

    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:12:y:2020:i:16:p:6422-:d:396834. 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.