IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v182y2024ics0965856424000636.html
   My bibliography  Save this article

Trip misreporting mining and expansion method for household travel survey

Author

Listed:
  • Wang, Xiang
  • Tong, Jiaxin
  • Zong, Weiyan
  • Lv, Yanqing
  • Shen, Jiayan

Abstract

Trip misreporting involves respondents omitting or concealing actual travel activities during the household travel survey (HTS) process. This paper investigated the trip misreporting mining method within the context of HTS by integrating mobile phone signaling data. Additionally, a data-driven approach was proposed to expand the HTS sample by incorporating trip misreporting and travel characteristics, including travel purpose and mode. The first step involved identifying stationary points through mobile phone signaling data and matching them with HTS data to uncover instances of trip misreporting. Subsequently, Point of Interest (POI) data was utilized to identify travel purposes. By integrating travel characteristic indicators from mobile phone signaling data, a Light Gradient Boosting Machine (Light-GBM) was employed for estimating travel modes. Lastly, a novel method for weighted combination expansion sampling was introduced. This method integrates traditional household and person samples with travel purposes and modes. The study revealed that in Suzhou, China, 23% of total trips went unrecorded in HTS due to trip misreporting. The accuracy of identifying travel purpose and estimating travel mode was 86.9% and 84%, respectively. Compared to the traditional method, this approach yielded a more accurate representation of actual travel distance and time. This reduced the distribution error by 7.4% and 6.2%, respectively. Additionally, six days of metro smart card data from weekdays, weekends, and holidays were utilized to validate daily passenger volume after sample expansion, resulting in a daily bias rate below 5%. The findings can effectively uncover the phenomenon of trip misreporting within HTS and provide innovative approaches to expanding the survey data, thereby enabling a more comprehensive analysis of the urban travel patterns.

Suggested Citation

  • Wang, Xiang & Tong, Jiaxin & Zong, Weiyan & Lv, Yanqing & Shen, Jiayan, 2024. "Trip misreporting mining and expansion method for household travel survey," Transportation Research Part A: Policy and Practice, Elsevier, vol. 182(C).
  • Handle: RePEc:eee:transa:v:182:y:2024:i:c:s0965856424000636
    DOI: 10.1016/j.tra.2024.104015
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856424000636
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tra.2024.104015?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Stopher, Peter R. & Greaves, Stephen P., 2007. "Household travel surveys: Where are we going?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(5), pages 367-381, June.
    2. Egu, Oscar & Bonnel, Patrick, 2020. "How comparable are origin-destination matrices estimated from automatic fare collection, origin-destination surveys and household travel survey? An empirical investigation in Lyon," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 267-282.
    3. Md. Sakoat Hossan & Hamidreza Asgari & Xia Jin, 2018. "Trip misreporting forecast using count data model in a GPS enhanced travel survey," Transportation, Springer, vol. 45(6), pages 1687-1700, November.
    4. Yusak O. Susilo & Chengxi Liu & Maria Börjesson, 2019. "The changes of activity-travel participation across gender, life-cycle, and generations in Sweden over 30 years," Transportation, Springer, vol. 46(3), pages 793-818, June.
    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. Vytautas Dumbliauskas & Vytautas Grigonis, 2020. "An Empirical Activity Sequence Approach for Travel Behavior Analysis in Vilnius City," Sustainability, MDPI, vol. 12(2), pages 1-22, January.
    2. Jariyasunant, Jerald & Carrel, Andre & Ekambaram, Venkatesan & Gaker, DJ & Kote, Thejovardhana & Sengupta, Raja & Walker, Joan L., 2011. "The Quantified Traveler: Using personal travel data to promote sustainable transport behavior," University of California Transportation Center, Working Papers qt9jg0p1rj, University of California Transportation Center.
    3. Gingerich, Kevin & Maoh, Hanna & Anderson, William, 2016. "Expansion of a GPS Truck Trip Sample to Remove Bias and Obtain Representative Flows for Ontario," 57th Transportation Research Forum (51st CTRF) Joint Conference, Toronto, Ontario, May 1-4, 2016 319310, Transportation Research Forum.
    4. Aihua Fan & Xumei Chen, 2020. "Exploring the Relationship between Transport Interventions, Mode Choice, and Travel Perception: An Empirical Study in Beijing, China," IJERPH, MDPI, vol. 17(12), pages 1-19, June.
    5. Li, Linchao & Zhu, Jiasong & Zhang, Hailong & Tan, Huachun & Du, Bowen & Ran, Bin, 2020. "Coupled application of generative adversarial networks and conventional neural networks for travel mode detection using GPS data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 136(C), pages 282-292.
    6. Chen, Cynthia & Gong, Hongmian & Lawson, Catherine & Bialostozky, Evan, 2010. "Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(10), pages 830-840, December.
    7. Winters, Meghan & Voss, Christine & Ashe, Maureen C. & Gutteridge, Kaitlyn & McKay, Heather & Sims-Gould, Joanie, 2015. "Where do they go and how do they get there? Older adults' travel behaviour in a highly walkable environment," Social Science & Medicine, Elsevier, vol. 133(C), pages 304-312.
    8. Jariyasunant, Jerald & Carrel, Andre & Ekambaram, Venkatesan & Gaker, DJ & Kote, Thejovardhana & Sengupta, Raja & Walker, Joan L., 2011. "The Quantified Traveler: Using personal travel data to promote sustainable transport behavior," University of California Transportation Center, Working Papers qt678537sx, University of California Transportation Center.
    9. Patrick Bonnel & Etienne Hombourger & Ana-Maria Olteanu-Raimond & Zbigniew Smoreda, 2015. "Passive Mobile Phone Dataset to Construct Origin-destination Matrix: Potentials and Limitations," Post-Print halshs-01664219, HAL.
    10. Lukas Hartwig & Reinhard Hössinger & Yusak Octavius Susilo & Astrid Gühnemann, 2022. "The Impacts of a COVID-19 Related Lockdown (and Reopening Phases) on Time Use and Mobility for Activities in Austria—Results from a Multi-Wave Combined Survey," Sustainability, MDPI, vol. 14(12), pages 1-24, June.
    11. Michael Adjemian & Jeffrey Williams, 2009. "Using census aggregates to proxy for household characteristics: an application to vehicle ownership," Transportation, Springer, vol. 36(2), pages 223-241, March.
    12. Nina Verzosa & Stephen Greaves & Chinh Ho & Mark Davis, 2021. "Stated willingness to participate in travel surveys: a cross-country and cross-methods comparison," Transportation, Springer, vol. 48(3), pages 1311-1327, June.
    13. Hannah Badland & Phil Donovan & Suzanne Mavoa & Melody Oliver & Moushumi Chaudhury & Karen Witten, 2015. "Assessing neighbourhood destination access for children: development of the NDAI-C audit tool," Environment and Planning B, , vol. 42(6), pages 1148-1160, November.
    14. Nigro, Marialisa & Castiglione, Marisdea & Maria Colasanti, Fabio & De Vincentis, Rosita & Valenti, Gaetano & Liberto, Carlo & Comi, Antonio, 2022. "Exploiting floating car data to derive the shifting potential to electric micromobility," Transportation Research Part A: Policy and Practice, Elsevier, vol. 157(C), pages 78-93.
    15. Egu, Oscar & Bonnel, Patrick, 2020. "How comparable are origin-destination matrices estimated from automatic fare collection, origin-destination surveys and household travel survey? An empirical investigation in Lyon," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 267-282.
    16. Andreas Dypvik Landmark & Petter Arnesen & Carl-Johan Södersten & Odd André Hjelkrem, 2021. "Mobile phone data in transportation research: methods for benchmarking against other data sources," Transportation, Springer, vol. 48(5), pages 2883-2905, October.
    17. Wati, Kala & Tranter, Paul J., 2015. "Spatial and socio-demographic determinants of South East Queensland students’ school cycling," Journal of Transport Geography, Elsevier, vol. 47(C), pages 23-36.
    18. Deschaintres, Elodie & Morency, Catherine & Trépanier, Martin, 2022. "Cross-analysis of the variability of travel behaviors using one-day trip diaries and longitudinal data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 163(C), pages 228-246.
    19. Cook, Jonathan A. & Sanchirico, James N. & Salon, Deborah & Williams, Jeffrey, 2015. "Empirical distributions of vehicle use and fuel efficiency across space: Implications of asymmetry for measuring policy incidence," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 187-199.
    20. Broach, Joseph & Dill, Jennifer & McNeil, Nathan Winslow, 2019. "Travel mode imputation using GPS and accelerometer data from a multi-day travel survey," Journal of Transport Geography, Elsevier, vol. 78(C), pages 194-204.

    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:transa:v:182:y:2024:i:c:s0965856424000636. 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: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    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.