Skip to main content
SpringerLink
Log in

How Far Are We From Transportation Equity? Measuring the Effect of Wheelchair Use on Daily Activity Patterns

  • Conference paper
  • First Online:
Proceedings of the 12th International Scientific Conference on Mobility and Transport

Part of the book series: Lecture Notes in Mobility ((LNMOB))

  • 265 Accesses

Abstract

The mobility needs of individuals with travel-limiting disabilities has been a transportation policy priority in the United States for more than thirty years, but efforts to model the behavioral implications of disability on travel have been limited. In this research, we present a daily activity pattern choice model for multiple person type segments including an individual’s wheelchair use as an explanatory variable. The model results show a strong negative impact of wheelchair use on out-of-home travel, exceeding the impact of other variables commonly considered in such models. We then apply the estimated model within an activity-based model for the Wasatch Front region in Utah; the results suggest a shift in tour making of sufficient scale—among both wheelchair users and those in their households—to warrant further scrutiny and analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    This implementation is not the official regional travel demand model; the calibration and development of this model is described in [19].

References

  1. AMPO (2020) Activitysim: an open platform for activity-based travel modeling. https://activitysim.github.io/

  2. Arel-Bundock V (2021) modelsummary: summary tables and plots for statistical models and data: beautiful, customizable, and publication-ready. https://vincentarelbundock.github.io/modelsummary/. R package version 0.8.0

  3. Bascom GW, Christensen KM (2017) The impacts of limited transportation access on persons with disabilities’ social participation. J Transport Health 7:227–234. ISSN 2214-1405. https://doi.org/10.1016/J.JTH.2017.10.002. https://www.sciencedirect.com/science/article/pii/S2214140517300075

  4. Bills TS, Sall EA, Walker JL (2012) Activity-based travel models and transportation equity analysis: research directions and exploration of model performance. Transp Res Record 2320(1):18–27

    Google Scholar 

  5. Bischoff JF (2019) Mobility as a service and the transition to driverless systems. PhD thesis, Technische Universität Berlin

    Google Scholar 

  6. Bradley M, Vovsha P (2005) A model for joint choice of daily activity pattern types of household members. Transportation 32(5):545–571. ISSN 00494488. https://doi.org/10.1007/s11116-005-5761-0

  7. Brumbaugh S (2018) Issue brief travel patterns of American adults with disabilities. Technical report

    Google Scholar 

  8. Yves C (2020) Estimation of random utility models in R: the mlogit package. J Stat Softw 95(11):1–41. https://doi.org/10.18637/jss.v095.i11

  9. Davidson W, Vovsha P, Freedman J, Donnelly R (2010) Ct-ramp family of activity-based models. In: Proceedings of the 33rd Australasian transport research forum (ATRF), vol 29. Citeseer, p 29

    Google Scholar 

  10. Domencich TA, McFadden D (1975) Urban travel demand: a behavioral analysis. North-Holland Pub Co. ISBN 0720431964. https://trid.trb.org/view/1175810

  11. Dunnington D (2021) ggspatial: spatial data framework for ggplot2. https://CRAN.R-project.org/package=ggspatial. R package version 1.1.5

  12. Erhardt G, Ory D, Sarvepalli A, Freedman J, Hood J, Stabler B (2012) Mtc’s travel model one: applications of an activity-based model in its first year. In: 5th transportation research board innovations in travel modeling conference

    Google Scholar 

  13. Ermagun A, Hajivosough S, Samimi A, Rashidi TH (2016) A joint model for trip purpose and escorting patterns of the disabled. Travel Behav Soc 3:51–58. ISSN 2214367X. https://doi.org/10.1016/j.tbs.2015.08.002

  14. Federal Transit Administration (2015) Americans with disabilities act: guidance. Circular FTA C 4710.1. https://www.transit.dot.gov/regulations-and-guidance/fta-circulars/americans-disabilities-act-guidance-pdf

  15. Feeley C (2010) Evaluating the transportation needs and accessibility issues for adults on the autism spectrum in New Jersey. In: Transportation research board annual meeting, 2010

    Google Scholar 

  16. Frackelton A, Grossman A, Palinginis E, Castrillon F, Elango V, Guensler R (2013) Measuring walkability: development of an automated sidewalk quality assessment tool. Suburban Sustain 1(1). http://dx.doi.org/10.5038/2164-0866.1.1.4

  17. Laplante M (2003) Demographics of wheeled mobility device users. In: Proceedings of the conference on space requirements for wheeled mobility, pp 1–23

    Google Scholar 

  18. Lubin A, Deka D (2012) Role of public transportation as job access mode. Transp Res Record (2277):90–97. ISSN 03611981. https://doi.org/10.3141/2277-11

  19. Macfarlane GS, Lant NJ (2021) Estimation and simulation of daily activity patterns for individuals using wheelchairs. Report, Utah Department of Transportation Research Report No. UT-21.10. https://rosap.ntl.bts.gov/view/dot/56982

  20. Macfarlane GS, Hunter C, Martinez A, Smith E (2021) Rider perceptions of an on-demand microtransit service in salt lake county, utah. Smart Cities 4(2):717–727. ISSN 2624-6511. https://www.mdpi.com/2624-6511/4/2/36

  21. Paul BM, Doyle J, Stabler B, Freedman J, Bettinardi A (2018). Multi-level population synthesis using entropy maximization-based simultaneous list balancing. In: Transportation research board annual meeting

    Google Scholar 

  22. R Core Team (2021) R: a language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. https://www.R-project.org/

  23. Soora R, Harry T (2014) Activity-based models of travel demand: promises, progress and prospects. Int J Urban Sci 18(1):31–60. https://doi.org/10.1080/12265934.2013.835118

    Article  Google Scholar 

  24. Rosenbloom S (2007) Transportation patterns and problems of people with disabilities. In: Field MJ, Jette AM (eds) The future of disability in America. National Academies Press, pp 1–592. ISBN 0309668646. https://doi.org/10.17226/11898. https://www.nap.edu/catalog/11898/the-future-of-disability-in-america

  25. Ruvolo M (2020) Access denied? Perceptions of new mobility services among disabled people in San Francisco. In: The SAGE encyclopedia of higher education, p 52. https://doi.org/10.4135/9781529714395.n81. https://escholarship.org/uc/item/6jv123qg

  26. Schmöcker JD, Quddus MA, Noland RB, Bell MGH (2005) Estimating trip generation of elderly and disabled people: analysis of London data. Transp Res Record (1924):9–18. ISSN 03611981. https://doi.org/10.3141/1924-02

  27. Shaheen S, Chan N (2016) Mobility and the sharing economy: potential to facilitate the first-and last-mile public transit connections. Built Environ 42(4):573–588. ISSN 0263-7960

    Google Scholar 

  28. Sweeney M (2004) Travel patterns of older americans with disabilities. Working paper 2004-001-OAS, Bureau of Transportation Statistics, pp 1–36

    Google Scholar 

  29. US Census Bureau (2022) Acs pums: American community survey public use microdata sample, 2022

    Google Scholar 

  30. U.S. Department of Transportation and Federal Highway Administration. 2017 national household travel survey, 2017. http://nhts.ornl.gov

  31. Van Roosmalen L, Paquin GJ, Steinfeld AM (2010) Quality of life technology: the state of personal transportation. Phys Med Rehab Clin North America 21(1):111–125. ISSN 10479651. https://doi.org/10.1016/j.pmr.2009.07.009

  32. Velho R, Holloway C, Symonds A, Balmer B (2016) The effect of transport accessibility on the social inclusion of wheelchair users: a mixed method analysis. Soc Inclus 4(3):24–35. ISSN 21832803. https://doi.org/10.17645/si.v4i3.484

  33. Vyas G, Vovsha P, Paleti R, Givon D, Birotker Y (2015) Investigation of alternative methods for modeling joint activity participation. Transp Res Record 2493(1):19–28. https://doi.org/10.3141/2493-03. https://www.journals.sagepub.com/doi/abs/10.3141/2493-03

  34. Wasfi R, El-geneidy A (2007) Measuring the transportation needs of people with developmental disabilities. The Lancet 369(9560):457. ISSN 01406736. https://doi.org/10.1016/S0140-6736(07)60218-9

  35. Wickham H, Chang W, Henry L, Pedersen TL, Takahashi K, Wilke C, Woo K, Yutani H, Dunnington D (2020) ggplot2: create elegant data visualisations using the grammar of graphics. https://CRAN.R-project.org/package=ggplot2. R package version 3.3.3

Download references

Acknowledgements

Figures and tables in this paper were created with a variety of R packages [2, 11, 35]. The authors would like to thank Chris Day and Christian Hunter for their help in preparing this analysis. This work was supported by the Utah Department of Transportation and the Federal Transit Administration. The authors alone are responsible for the preparation and conclusions presented herein. The contents do not necessarily reflect the views, opinions, endorsements, or policies of the Utah Department of Transportation of the US Department of Transportation. The Utah Department of Transportation makes no representation or warranty of any kind, and assumes no liability therefore.

Author information

Authors and Affiliations

  1. Civil and Construction Engineering Department, Brigham Young University, 430 Engineering Building Provo, Utah, 84602, USA

    Gregory S. Macfarlane & Nate Lant

Authors
  1. Gregory S. Macfarlane
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nate Lant
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gregory S. Macfarlane .

Editor information

Editors and Affiliations

  1. TUM School of Engineering and Design, Technical University of Munich, Munich, Germany

    Constantinos Antoniou

  2. TUM School of Engineering and Design, Technical University of Munich, Munich, Germany

    Fritz Busch

  3. TUM Asia Pte Ltd., Singapore, Singapore

    Andreas Rau

  4. Railway Engineering, TUM Asia Pte Ltd., Singapore, Singapore

    Mahesh Hariharan

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Macfarlane, G.S., Lant, N. (2023). How Far Are We From Transportation Equity? Measuring the Effect of Wheelchair Use on Daily Activity Patterns. In: Antoniou, C., Busch, F., Rau, A., Hariharan, M. (eds) Proceedings of the 12th International Scientific Conference on Mobility and Transport. Lecture Notes in Mobility. Springer, Singapore. https://doi.org/10.1007/978-981-19-8361-0_10

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-8361-0_10

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-8360-3

  • Online ISBN: 978-981-19-8361-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation