IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i20p13448-d946456.html
   My bibliography  Save this article

Exploration and Prediction of the Elderly Travel Behavior Based on a Novel GR-GA-BP Hybrid Model

Author

Listed:
  • Zhihong Li

    (Department of Transportation, Beijing University of Civil Engineering and Architecture, Beijing 100044, China)

  • Zinan Wang

    (Department of Transportation, Beijing University of Civil Engineering and Architecture, Beijing 100044, China)

  • Yanjie Wen

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China)

  • Li Zhao

    (China Academy of Urban Planning Design, Beijing 100044, China)

Abstract

With the aging trend in megacities, the travel behaviors of the elderly have attracted much attention. Accurate prediction of the travel behaviors of the elderly is a key link to meet the traffic demand and public facilities’ optimization. The aim of this paper was to explore the link between the travel characteristics and variables of the daily activities of the elderly. Based on a stratified sampling survey, the internal relationship between the characteristics of the elderly and their travel behavior was studied and discussed in this work. A novel grey correlation degree–genetic algorithm–back propagation (GR-GA-BP) hybrid model was proposed to predict the travel behavior of the elderly. Then, a grey correlation degree module was established and used to analyze the correlation between the individual elderly characteristics and their travel behavior. The results showed the following: (1) Both the times of weekly trips (y1) and average round-trip travel time (y2) were highly sensitive to the external environment, especially buses, subway stations, and recreational facilities. The size of the family was less sensitive to the travel behavior. (2) Referring to prediction of the times of weekly trips, the MRE of the proposed model was 23.12%, which was 15.22% less than the baseline models. (3) In terms of the prediction of round-trip travel time, the MRE of the proposed model was 7.13%, which was 14.00–69.41% lower than the baseline models. (4) The times of trips per week were 3.5. In summary, this paper provides technical support for formulating traffic demand policies and facilitates the configuration of cities for an aging society.

Suggested Citation

  • Zhihong Li & Zinan Wang & Yanjie Wen & Li Zhao, 2022. "Exploration and Prediction of the Elderly Travel Behavior Based on a Novel GR-GA-BP Hybrid Model," Sustainability, MDPI, vol. 14(20), pages 1-17, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13448-:d:946456
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/20/13448/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/20/13448/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liu, Shasha & Yamamoto, Toshiyuki & Yao, Enjian & Nakamura, Toshiyuki, 2021. "Examining public transport usage by older adults with smart card data: A longitudinal study in Japan," Journal of Transport Geography, Elsevier, vol. 93(C).
    2. Geoffrey C. Smith & Gina M. Sylvestre, 2001. "Determinants of the Travel Behavior of the Suburban Elderly," Growth and Change, Wiley Blackwell, vol. 32(3), pages 395-412.
    3. Lachapelle, Ugo & Cloutier, Marie-Soleil, 2017. "On the complexity of finishing a crossing on time: Elderly pedestrians, timing and cycling infrastructure," Transportation Research Part A: Policy and Practice, Elsevier, vol. 96(C), pages 54-63.
    4. Titheridge, Helena & Achuthan, Kamalasudhan & Mackett, Roger L & Solomon, Juliet, 2009. "Assessing the extent of transport social exclusion among the elderly," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 2(2), pages 31-48.
    5. Israel Schwarzlose, Alicia A. & Mjelde, James W. & Dudensing, Rebekka M. & Jin, Yanhong & Cherrington, Linda K. & Chen, Junyi, 2014. "Willingness to pay for public transportation options for improving the quality of life of the rural elderly," Transportation Research Part A: Policy and Practice, Elsevier, vol. 61(C), pages 1-14.
    6. Lars Böcker & Patrick Amen & Marco Helbich, 2017. "Elderly travel frequencies and transport mode choices in Greater Rotterdam, the Netherlands," Transportation, Springer, vol. 44(4), pages 831-852, July.
    7. Dargay, Joyce M. & Clark, Stephen, 2012. "The determinants of long distance travel in Great Britain," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(3), pages 576-587.
    8. Stern, Steven, 1993. "A disaggregate discrete choice model of transportation demand by elderly and disabled people in rural Virginia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 27(4), pages 315-327, July.
    9. Wenzhi Liu & Huapu Lu & Zhiyuan Sun & Jing Liu, 2017. "Elderly’s Travel Patterns and Trends: The Empirical Analysis of Beijing," Sustainability, MDPI, vol. 9(6), pages 1-11, 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. Yu, Zhao & Zhao, Pengjun, 2021. "The factors in residents' mobility in rural towns of China: Car ownership, road infrastructure and public transport services," Journal of Transport Geography, Elsevier, vol. 91(C).
    2. Saidatulakmal Mohd & Abdul Rais Abdul Latiff & Abdelhak Senadjki, 2019. "Travel Behavior of Elderly in George Town and Malacca, Malaysia," Sustainability, MDPI, vol. 11(19), pages 1-20, September.
    3. Ranković Plazinić, Biljana & Jović, Jadranka, 2018. "Mobility and transport potential of elderly in differently accessible rural areas," Journal of Transport Geography, Elsevier, vol. 68(C), pages 169-180.
    4. Yang, Yongjiang & Sasaki, Kuniaki & Cheng, Long & Tao, Sui, 2022. "Does the built environment matter for active travel among older adults: Insights from Chiba City, Japan," Journal of Transport Geography, Elsevier, vol. 101(C).
    5. Moniruzzaman, Md. & Páez, Antonio & Nurul Habib, Khandker M. & Morency, Catherine, 2013. "Mode use and trip length of seniors in Montreal," Journal of Transport Geography, Elsevier, vol. 30(C), pages 89-99.
    6. Theo Arentze & Harry Timmermans & Peter Jorritsma & Marie-José Olde Kalter & Arnout Schoemakers, 2008. "More gray hair—but for whom? Scenario-based simulations of elderly activity travel patterns in 2020," Transportation, Springer, vol. 35(5), pages 613-627, August.
    7. Antonio Paez & Darren Scott & Dimitris Potoglou & Pavlos Kanaroglou & K. Bruce Newbold, 2007. "Elderly Mobility: Demographic and Spatial Analysis of Trip Making in the Hamilton CMA, Canada," Urban Studies, Urban Studies Journal Limited, vol. 44(1), pages 123-146, January.
    8. Urbanek, Anna & Acedański, Jan & Krawczyk, Grzegorz, 2023. "Depopulation or ageing? Decomposing the aggregate effects of projected demographic changes on urban transport systems," Journal of Transport Geography, Elsevier, vol. 111(C).
    9. Shao, Fengjing & Sui, Yi & Yu, Xiang & Sun, Rencheng, 2019. "Spatio-temporal travel patterns of elderly people – A comparative study based on buses usage in Qingdao, China," Journal of Transport Geography, Elsevier, vol. 76(C), pages 178-190.
    10. Kwang-Sub Lee & Jin Ki Eom & Jun Lee & Sangpil Ko, 2021. "Analysis of the Activity and Travel Patterns of the Elderly Using Mobile Phone-Based Hourly Locational Trajectory Data: Case Study of Gangnam, Korea," Sustainability, MDPI, vol. 13(6), pages 1-23, March.
    11. Zhao, Pengjun & Yu, Zhao, 2020. "Investigating mobility in rural areas of China: Features, equity, and factors," Transport Policy, Elsevier, vol. 94(C), pages 66-77.
    12. Samira Ramezani & Tiina Laatikainen & Kamyar Hasanzadeh & Marketta Kyttä, 2021. "Shopping trip mode choice of older adults: an application of activity space and hybrid choice models in understanding the effects of built environment and personal goals," Transportation, Springer, vol. 48(2), pages 505-536, April.
    13. 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.
    14. Su, Rongxiang & Xiao, Jingyi & McBride, Elizabeth C. & Goulias, Konstadinos G., 2021. "Understanding senior's daily mobility patterns in California using human mobility motifs," Journal of Transport Geography, Elsevier, vol. 94(C).
    15. Jean Ryan, 2020. "Examining the Process of Modal Choice for Everyday Travel Among Older People," IJERPH, MDPI, vol. 17(3), pages 1-19, January.
    16. Kent, Jennifer L. & Mulley, Corinne & Stevens, Nick, 2020. "Challenging policies that prohibit public transport use: Travelling with pets as a case study," Transport Policy, Elsevier, vol. 99(C), pages 86-94.
    17. Giménez-Nadal, José Ignacio & Velilla, Jorge & Ortega, Raquel, 2022. "Revisiting excess commuting and self-employment: The case of Latin America," GLO Discussion Paper Series 1179, Global Labor Organization (GLO).
    18. Abdul Rais Abdul Latiff & Saidatulakmal Mohd, 2023. "Transport, Mobility and the Wellbeing of Older Adults: An Exploration of Private Chauffeuring and Companionship Services in Malaysia," IJERPH, MDPI, vol. 20(3), pages 1-17, February.
    19. Lin, J. & Z. & Zhang, Z., 2018. "Does transportation matter to tobacco leaf producers? Evidence from Guizhou, China," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 276998, International Association of Agricultural Economists.
    20. Tzu-Ying Chen & Rong-Chang Jou & Yi-Chang Chiu, 2021. "Using the Multilevel Random Effect Model to Analyze the Behavior of Carpool Users in Different Cities," Sustainability, MDPI, vol. 13(2), pages 1-13, January.

    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:14:y:2022:i:20:p:13448-:d:946456. 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.