IDEAS home Printed from https://ideas.repec.org/a/eee/jotrge/v93y2021ics096669232100123x.html
   My bibliography  Save this article

Analysis of travel mode choice and trip chain pattern relationships based on multi-day GPS data: A case study in Shanghai, China

Author

Listed:
  • Huang, Yuqiao
  • Gao, Linjie
  • Ni, Anning
  • Liu, Xiaoning

Abstract

As the propensity to link multiple intermediate stops in a trip chain (a sequence of journeys that starts and ends at home, includes visiting one or more locations) is more prevalent, the relationship between travel mode choice and trip chain pattern aroused the attention of academics. This paper examines two distinct structures to identify the decision process of travelers between travel mode choice and trip chain pattern: one structure in which trip chain pattern organization precedes travel mode choice, another structure in which travel mode choice decision precedes the organization of trip chain pattern. To accommodate multi-day behavioral variability and unobserved heterogeneity in personal characteristics ignored by traditional travel surveys, multi-day GPS data collected in Shanghai is employed to estimate these two structures within Nested Logit (NL) model. The Monte Carlo (MC) method simulates the switch of trip-chaining and mode choice under possible Transportation Demand Management (TDM) strategies based on estimation results. The findings of this study are as follows: (1) trip chain pattern decision precedes travel mode choice, which means trip-chaining is organized first and affects travel mode choice; (2) complex trip chain is related to higher automobile dependency, and it is a barrier to the tendency to adopt public transit; (3) people who generally travel by automobiles might switch to public transit when private cars are unavailable, and an increase in household bicycle ownership enhances competition between the bicycle and public transit which leads people to turn to cycling. These findings help implement TDM strategies to develop sustainable transportation systems and optimize the urban trip structure.

Suggested Citation

  • Huang, Yuqiao & Gao, Linjie & Ni, Anning & Liu, Xiaoning, 2021. "Analysis of travel mode choice and trip chain pattern relationships based on multi-day GPS data: A case study in Shanghai, China," Journal of Transport Geography, Elsevier, vol. 93(C).
    • Handle: RePEc:eee:jotrge:v:93:y:2021:i:c:s096669232100123x
    DOI: 10.1016/j.jtrangeo.2021.103070
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096669232100123X
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.jtrangeo.2021.103070?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Md Hadiuzzaman & Nahid Parvez Farazi & Sanjana Hossain & Saurav Barua & Farzana Rahman, 2019. "Structural equation approach to investigate trip-chaining and mode choice relationships in the context of developing countries," Transportation Planning and Technology, Taylor & Francis Journals, vol. 42(4), pages 391-415, May.
    2. Jianchuan Xianyu & Soora Rasouli & Harry Timmermans, 2017. "Analysis of variability in multi-day GPS imputed activity-travel diaries using multi-dimensional sequence alignment and panel effects regression models," Transportation, Springer, vol. 44(3), pages 533-553, May.
    3. Bhat, Chandra, 1999. "An analysis of evening commute stop-making behavior using repeated choice observations from a multi-day survey," Transportation Research Part B: Methodological, Elsevier, vol. 33(7), pages 495-510, September.
    4. Currie, Graham & Delbosc, Alexa, 2011. "Exploring the trip chaining behaviour of public transport users in Melbourne," Transport Policy, Elsevier, vol. 18(1), pages 204-210, January.
    5. Yu Ding & Huapu Lu, 2017. "The interactions between online shopping and personal activity travel behavior: an analysis with a GPS-based activity travel diary," Transportation, Springer, vol. 44(2), pages 311-324, March.
    6. Adler, Thomas & Ben-Akiva, Moshe, 1979. "A theoretical and empirical model of trip chaining behavior," Transportation Research Part B: Methodological, Elsevier, vol. 13(3), pages 243-257, September.
    7. Kay Axhausen & Andrea Zimmermann & Stefan Schönfelder & Guido Rindsfüser & Thomas Haupt, 2002. "Observing the rhythms of daily life: A six-week travel diary," Transportation, Springer, vol. 29(2), pages 95-124, May.
    8. Long Cheng & Xuewu Chen & Shuo Yang, 2016. "An exploration of the relationships between socioeconomics, land use and daily trip chain pattern among low-income residents," Transportation Planning and Technology, Taylor & Francis Journals, vol. 39(4), pages 358-369, June.
    9. Daisy, Naznin Sultana & Millward, Hugh & Liu, Lei, 2018. "Trip chaining and tour mode choice of non-workers grouped by daily activity patterns," Journal of Transport Geography, Elsevier, vol. 69(C), pages 150-162.
    10. Robert Schlich & Kay Axhausen, 2003. "Habitual travel behaviour: Evidence from a six-week travel diary," Transportation, Springer, vol. 30(1), pages 13-36, February.
    11. Bhat, Chandra R., 2015. "A new generalized heterogeneous data model (GHDM) to jointly model mixed types of dependent variables," Transportation Research Part B: Methodological, Elsevier, vol. 79(C), pages 50-77.
    12. 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.
    13. Bhat, Chandra R., 1997. "Work travel mode choice and number of non-work commute stops," Transportation Research Part B: Methodological, Elsevier, vol. 31(1), pages 41-54, February.
    14. Cherchi, Elisabetta & Cirillo, Cinzia & Ortúzar, Juan de Dios, 2017. "Modelling correlation patterns in mode choice models estimated on multiday travel data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 96(C), pages 146-153.
    15. Jan-Dirk Schmöcker & Fengming Su & Robert Noland, 2010. "An analysis of trip chaining among older London residents," Transportation, Springer, vol. 37(1), pages 105-123, January.
    16. Hensher, David A. & Ton, Tu T., 2000. "A comparison of the predictive potential of artificial neural networks and nested logit models for commuter mode choice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 36(3), pages 155-172, September.
    17. Li, Zhibin & Wang, Wei & Yang, Chen & Jiang, Guojun, 2013. "Exploring the causal relationship between bicycle choice and trip chain pattern," Transport Policy, Elsevier, vol. 29(C), pages 170-177.
    18. Gan, Li Ping & Recker, Will, 2008. "A mathematical programming formulation of the household activity rescheduling problem," Transportation Research Part B: Methodological, Elsevier, vol. 42(6), pages 571-606, July.
    19. Florian Schneider & Danique Ton & Lara-Britt Zomer & Winnie Daamen & Dorine Duives & Sascha Hoogendoorn-Lanser & Serge Hoogendoorn, 2021. "Trip chain complexity: a comparison among latent classes of daily mobility patterns," Transportation, Springer, vol. 48(2), pages 953-975, April.
    20. Lee, Yuhwa & Hickman, Mark & Washington, Simon, 2007. "Household type and structure, time-use pattern, and trip-chaining behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(10), pages 1004-1020, December.
    21. David Hensher & April Reyes, 2000. "Trip chaining as a barrier to the propensity to use public transport," Transportation, Springer, vol. 27(4), pages 341-361, December.
    22. Naznin Sultana Daisy & Lei Liu & Hugh Millward, 2020. "Trip chaining propensity and tour mode choice of out-of-home workers: evidence from a mid-sized Canadian city," Transportation, Springer, vol. 47(2), pages 763-792, April.
    23. Hess, Stephane & Daly, Andrew & Rohr, Charlene & Hyman, Geoff, 2007. "On the development of time period and mode choice models for use in large scale modelling forecasting systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(9), pages 802-826, November.
    24. 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.
    25. Joachim Scheiner & Christian Holz-Rau, 2007. "Travel mode choice: affected by objective or subjective determinants?," Transportation, Springer, vol. 34(4), pages 487-511, July.
    26. Ye, Xin & Pendyala, Ram M. & Gottardi, Giovanni, 2007. "An exploration of the relationship between mode choice and complexity of trip chaining patterns," Transportation Research Part B: Methodological, Elsevier, vol. 41(1), pages 96-113, January.
    27. Kang, Hejun & Scott, Darren M., 2010. "Exploring day-to-day variability in time use for household members," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(8), pages 609-619, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lingjuan Chen & Yijing Zhao & Zupeng Liu & Xinran Yang, 2022. "Construction of Commuters’ Multi-Mode Choice Model Based on Public Transport Operation Data," Sustainability, MDPI, vol. 14(22), pages 1-20, November.
    2. Yuan Yuan & Chunfu Shao & Zhichao Cao & Chaoying Yin, 2023. "The Effect of Travel-Chain Complexity on Public Transport Travel Intention: A Mixed-Selection Model," IJERPH, MDPI, vol. 20(5), pages 1-29, March.
    3. Li, Xiaohui & Wang, Zhenpo & Zhang, Lei & Sun, Fengchun & Cui, Dingsong & Hecht, Christopher & Figgener, Jan & Sauer, Dirk Uwe, 2023. "Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview," Energy, Elsevier, vol. 268(C).
    4. Bi, Hui & Ye, Zhirui & Hu, Liyang & Zhu, He, 2021. "Why they don't choose bus service? Understanding special online car-hailing behavior near bus stops," Transport Policy, Elsevier, vol. 114(C), pages 280-297.
    5. Zhang, Yiyuan & Luo, Xia & Qiu, Yuansen & Fu, Yuxue, 2022. "Understanding the generation mechanism of BEV drivers' charging demand: An exploration of the relationship between charging choice and complexity of trip chaining patterns," Transportation Research Part A: Policy and Practice, Elsevier, vol. 158(C), pages 110-126.
    6. Patricia Gálvez-Fernández & Manuel Herrador-Colmenero & Pablo Campos-Garzón & Daniel Molina-Soberanes & Romina Gisele Saucedo-Araujo & María Jesús Aranda-Balboa & Amador Jesús Lara-Sánchez & Víctor Se, 2022. "Convergent Validation of a Self-Reported Commuting to and from School Diary in Spanish Adolescents," IJERPH, MDPI, vol. 20(1), pages 1-15, December.
    7. Tsoleridis, Panagiotis & Choudhury, Charisma F. & Hess, Stephane, 2022. "Deriving transport appraisal values from emerging revealed preference data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 225-245.
    8. Wenjing Wang & Yanyan Chen & Haodong Sun & Yusen Chen, 2021. "Multiple Binary Classification Model of Trip Chain Based on the Fusion of Internet Location Data and Transport Data," Sustainability, MDPI, vol. 13(21), pages 1-15, November.
    9. Hongjun Cui & Mingzhi Li & Minqing Zhu & Xinwei Ma, 2023. "Investigating the Impacts of Urban–Rural Bus Service Quality on Rural Residents’ Travel Choices Using an SEM–MNL Integration Model," Sustainability, MDPI, vol. 15(15), pages 1-22, August.

    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. Wang, Rui, 2015. "The stops made by commuters: evidence from the 2009 US National Household Travel Survey," Journal of Transport Geography, Elsevier, vol. 47(C), pages 109-118.
    2. Liya Yang & Lingqian Hu & Zhenbo Wang, 2019. "The built environment and trip chaining behaviour revisited: The joint effects of the modifiable areal unit problem and tour purpose," Urban Studies, Urban Studies Journal Limited, vol. 56(4), pages 795-817, March.
    3. Sottile, Eleonora & Tuveri, Giovanni & Piras, Francesco & Meloni, Italo, 2022. "Modelling commuting tours versus non-commuting tours for university students. A panel data analysis from different contexts," Transport Policy, Elsevier, vol. 118(C), pages 56-67.
    4. Rafiq, Rezwana & McNally, Michael G., 2022. "A structural analysis of the work tour behavior of transit commuters," Transportation Research Part A: Policy and Practice, Elsevier, vol. 160(C), pages 61-79.
    5. Wan, Li & Tang, Junqing & Wang, Lihua & Schooling, Jennifer, 2021. "Understanding non-commuting travel demand of car commuters – Insights from ANPR trip chain data in Cambridge," Transport Policy, Elsevier, vol. 106(C), pages 76-87.
    6. Zidan Mao & Dick Ettema & Martin Dijst, 2018. "Analysis of travel time and mode choice shift for non-work stops in commuting: case study of Beijing, China," Transportation, Springer, vol. 45(3), pages 751-766, May.
    7. 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.
    8. Florian Schneider & Danique Ton & Lara-Britt Zomer & Winnie Daamen & Dorine Duives & Sascha Hoogendoorn-Lanser & Serge Hoogendoorn, 2021. "Trip chain complexity: a comparison among latent classes of daily mobility patterns," Transportation, Springer, vol. 48(2), pages 953-975, April.
    9. Michael Duncan, 2016. "How much can trip chaining reduce VMT? A simplified method," Transportation, Springer, vol. 43(4), pages 643-659, July.
    10. Ge Gao & Huijun Sun & Jianjun Wu, 2019. "Activity-based trip chaining behavior analysis in the network under the parking fee scheme," Transportation, Springer, vol. 46(3), pages 647-669, June.
    11. De Witte, Astrid & Hollevoet, Joachim & Dobruszkes, Frédéric & Hubert, Michel & Macharis, Cathy, 2013. "Linking modal choice to motility: A comprehensive review," Transportation Research Part A: Policy and Practice, Elsevier, vol. 49(C), pages 329-341.
    12. Xin Guan & Xin Ye & Cheng Shi & Yajie Zou, 2019. "A Multivariate Modeling Analysis of Commuters’ Non-Work Activity Allocations in Xiaoshan District of Hangzhou, China," Sustainability, MDPI, vol. 11(20), pages 1-19, October.
    13. Bautista-Hernández, Dorian Antonio, 2022. "Individual, household, and urban form determinants of trip chaining of non-work travel in México City," Journal of Transport Geography, Elsevier, vol. 98(C).
    14. 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.
    15. Li, Zhibin & Wang, Wei & Yang, Chen & Jiang, Guojun, 2013. "Exploring the causal relationship between bicycle choice and trip chain pattern," Transport Policy, Elsevier, vol. 29(C), pages 170-177.
    16. João De Abreu e Silva, 2018. "The Effects of Land-Use Patterns on Home-Based Tour Complexity and Total Distances Traveled: A Path Analysis," Sustainability, MDPI, vol. 10(3), pages 1-16, March.
    17. 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.
    18. 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.
    19. François Sprumont & Ariane Scheffer & Geoffrey Caruso & Eric Cornelis & Francesco Viti, 2022. "Quantifying the Relation between Activity Pattern Complexity and Car Use Using a Partial Least Square Structural Equation Model," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
    20. Manoj, M. & Verma, Ashish, 2015. "Activity–travel behaviour of non-workers from Bangalore City in India," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 400-424.

    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:eee:jotrge:v:93:y:2021:i:c:s096669232100123x. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/journal-of-transport-geography .

    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.