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Transportation

, Volume 45, Issue 5, pp 1207–1229 | Cite as

Exploring the impact of walk–bike infrastructure, safety perception, and built-environment on active transportation mode choice: a random parameter model using New York City commuter data

  • H. M. Abdul Aziz
  • Nicholas N. Nagle
  • April M. Morton
  • Michael R. Hilliard
  • Devin A. White
  • Robert N. Stewart
Article

Abstract

This study estimates a random parameter (mixed) logit model for active transportation (walk and bicycle) choices for work trips in the New York City (using 2010–2011 Regional Household Travel Survey Data). We explored the effects of traffic safety, walk–bike network facilities, and land use attributes on walk and bicycle mode choice decision in the New York City for home-to-work commute. Applying the flexible econometric structure of random parameter models, we capture the heterogeneity in the decision making process and simulate scenarios considering improvement in walk–bike infrastructure such as sidewalk width and length of bike lane. Our results indicate that increasing sidewalk width, total length of bike lane, and proportion of protected bike lane will increase the likelihood of more people taking active transportation mode This suggests that the local authorities and planning agencies to invest more on building and maintaining the infrastructure for pedestrians. Further, improvement in traffic safety by reducing traffic crashes involving pedestrians and bicyclists, will increase the likelihood of taking active transportation modes. Our results also show positive correlation between number of non-motorized trips by the other family members and the likelihood to choose active transportation mode. The model would be an essential tool to estimate the impact of improving traffic safety and walk–bike infrastructure which will assist in investment decision making.

Keywords

Active transportation Travel behavior Random parameter model Walking Bicycling New York City 

Notes

Acknowledgements

This material is based upon work supported by the US Department of Energy, Office of Science under contract number DE-AC05-00OR22725. This work is funded by LDRD-32112540. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the US Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

References

  1. Adkins, A., Dill, J., Luhr, G., Neal, M.: Unpacking walkability: testing the influence of urban design features on perceptions of walking environment attractiveness. J. Urban Des. 17(4), 499–510 (2012)CrossRefGoogle Scholar
  2. Agrawal, A.W., Schimek, P.: Extent and correlates of walking in the USA. Transp. Res. Part D Transp. Environ. 12, 548–563 (2007). http://linkinghub.elsevier.com/retrieve/pii/S1361920907000788
  3. Anastasopoulos, P.C., Mannering, F.L.: An empirical assessment of fixed and random parameter logit models using crash- and non-crash-specific injury data. Accident Anal. Prev. 43(3), 1140–1147 (2011)CrossRefGoogle Scholar
  4. Aziz, H.M.A., Ukkusuri, S.V., Hasan, S.: Exploring the determinants of pedestrian-vehicle crash severity in New York City. Accident Anal. Prev. 50, 1298–1309 (2013)CrossRefGoogle Scholar
  5. Bhat, C.R.: Simulation estimation of mixed discrete choice models using randomized and scrambled halton sequences. Transp. Res. Part B Methodol. 37(9), 837–855 (2003)CrossRefGoogle Scholar
  6. Buehler, R., Pucher, J.: Cycling to work in 90 large American cities: new evidence on the role of bike paths and lanes. Transportation 39(2), 409–432 (2011). doi: 10.1007/s11116-011-9355-8 CrossRefGoogle Scholar
  7. Buehler, R., Pucher, J.: Walking and cycling in Western Europe and the United States. TR NEWS 280 May–June (2012)Google Scholar
  8. Clifton, K.J., Dill, J.: Women’s travel behavior and land use: will new styles of neighborhoods lead to more women walking? In: Conference on Research on Women’s Issues in Transportation, vol. 2, p. 35 (2005). http://trid.trb.org/view.aspx?id=773072
  9. CUTR.: Public transit in America: findings from the 1995 nationwide personal transportation survey. Center For Urban Transportation Research. Table 4–13 (1998). www.cutr.usf.edu
  10. Dill, J., Carr, T.: Bicycle commuting and facilities in major U.S. cities: if you build them, commuters will use them. Transp. Res. Rec. 1828(1), 116–123 (2003). http://trb.metapress.com/openurl.asp?genre=article&id=doi:10.3141/1828-14
  11. Ferdous, N., Pendyala, R.M., Bhat, C.R., Konduri, K.C.: Modeling the influence of family, social context, and spatial proximity on use of nonmotorized transport mode. Transp. Res. Rec. 2230, 111–120 (2011)CrossRefGoogle Scholar
  12. Greene, W.H.: NLOGIT: Version 5.0: reference guide. Econometric Software Inc, UK (2012)Google Scholar
  13. Halldórsdóttir, K., Christensen, L., Jensen, T.C., Prato, C.G.: Modelling mode choice in short trips-shifting from car to bicycle. In: ETC (2011)Google Scholar
  14. Handy, S., Cao, X., Mokhtarian, P.L.: Self-selection in the relationship between the built environment and walking: empirical evidence from Northern California. J. Am. Plan. Assoc. 72(1), 55–74 (2006). http://www.tandfonline.com/doi/abs/10.1080/01944360608976724
  15. Handy, S., Mokhtarian, P.: Residential location choice and travel behavior: implications for air quality. Prepared for the ... (2004). http://pubs.its.ucdavis.edu/download_pdf.php?id=1759
  16. Handy, S.L., Xing, Y.: Factors correlated with bicycle commuting: a study in six small U.S. cities. Int. J. Sustain. Transp. 5(2), 91–110 (2011). http://www.tandfonline.com/doi/abs/10.1080/15568310903514789
  17. Hensher, D.A., Greene, W.H.: The mixed logit model: the state of practice. Transportation 30(2), 133–176 (2003)CrossRefGoogle Scholar
  18. Hensher, D.A., Rose, J.M., Greene, W.H.: Combining rp and sp data: biases in using the nested logit trick-contrasts with flexible mixed logit incorporating panel and scale effects. J. Transp. Geogr. 16(2), 126–133 (2008)CrossRefGoogle Scholar
  19. Ho, C., Mulley, C.: Intra-household interactions in transport research: a review. Transp. Rev. Transl. Transdiscip. J. 35(1), 33–55 (2015)Google Scholar
  20. Hood, J., Sall, E., Charlton, B.: A GPS-based bicycle route choice model for San Francisco, California. Transp. Lett. 3(1), 63–75 (2011)CrossRefGoogle Scholar
  21. Iacono, M., Krizek, K.J., El-Geneidy, A.: Measuring non-motorized accessibility: issues, alternatives, and execution. J. Transp. Geogr. 18(1), 133–140 (2010). doi: 10.1016/j.jtrangeo.2009.02.002 CrossRefGoogle Scholar
  22. ITRF: IRTAD Road Safety Annual Report 2011. Tech. Rep., International Traffic Safety Data and Analysis Group (2011)Google Scholar
  23. Khan, M., Kockelman, K.M., Xiong, X.: Models for anticipating non-motorized travel choices, and the role of the built environment. Transp. Policy 35, 117–126 (2014). doi: 10.1016/j.tranpol.2014.05.008 CrossRefGoogle Scholar
  24. Kuzmyak, J.R., Walters, J., Bradley, M., Kockelman, K.M.: Estimating bicycling and walking for planning and project development: a guidebook. NCHRP Report 770, Transportation Research Board (2014)Google Scholar
  25. Litman, T.: Transportation cost and benefit analysis II: techniques, estimates, and implications. Victoria Transport Policy Institute (2009a). www.vtpi.org/tca. Accessed May 2015
  26. Litman, T.: Travel time cost (chapter 5)-transportation cost and benefit analysis: techniques, estimates, and implications. Victoria Transport Policy Institute (2009b). www.vtpi.org/tca. Accessed May 2015
  27. Liu, C., Susilo, Y.O., Karlström, A.: Examining the impact of weather variability on non-commuters’ daily activity-travel patterns in different regions of Sweden. J. Transp. Geogr. 39, 36–48 (2014)CrossRefGoogle Scholar
  28. McFadden, D., Train, K.: Mixed mnl models for discrete response. J. Appl. Econom. 15(5), 447–470 (2000)CrossRefGoogle Scholar
  29. Millward, H., Spinney, J., Scott, D.: Active-transport walking behavior: destinations, durations, distances. J. Transp. Geogr. 28, 101–110 (2013). http://linkinghub.elsevier.com/retrieve/pii/S096669231200289X
  30. Milne, A., Meline, M.: Bicycling and walking in the United States: 2014 Benchmarking report. Alliance for Biking & Walking (2014). www.BikeWalkAlliance.org/Benchmarking.org
  31. Munizaga, M.A., Alvarez-Daziano, R.: Mixed logit vs. nested logit and probit models. In: 5th Tri-annual Invitational Choice Symposium Workshop: Hybrid Choice Models, Formulation and Practical Issues, Asilomar (2001)Google Scholar
  32. NHI: Estimating the impacts of urban transportation alternatives. National Highway Institute FHWA, Course no. 15257, pp. 6–16 (1995)Google Scholar
  33. NYSDOT/NYMTC: 2010–2011 Regional household travel survey data users manual and public use data set. New York Metropolitan Transportation Council & North Jersey Transportation Planning Authority, Contract #C000780, PIN: PTCS08A01 and NJTPA Contract 11/205 Regional Household Travel Survey: NJTPA Component, 162 (2013)Google Scholar
  34. NYSDOT/NYMTC: North Jersey Transportation Planning Authority 2010/2011 Regional Household Travel Survey Final Report. Tech. Rep. October, New York Metropolitan Transportation Council & North Jersey Transportation Planning Authority (2014)Google Scholar
  35. Obichere, C., McKinney, L., Ryan, R., Toth, N.: Report on the Fiscal 2016 Preliminary Budget and the Fiscal 2015 Preliminary Mayors Management Report. Tech. Rep., Department of Transportation, New York City (2015)Google Scholar
  36. Plaut, P.O.: Non-motorized commuting in the US. Transp. Res. Part D Transp. Environ. 10(5), 347–356 (2005). http://linkinghub.elsevier.com/retrieve/pii/S1361920905000179
  37. Rodriguez, D.A., Joo, J.: The relationship between non-motorized mode choice and the local physical environment. Transp. Res. Part D Transp. Environ. 9(2), 151–173 (2004). http://www.sciencedirect.com/science/article/pii/S1361920903000889
  38. Salon, D.: Neighborhoods, cars, and commuting in New York City: a discrete choice approach. Transp. Res. Part A Policy Pract. 43(2), 180–196 (2009). http://linkinghub.elsevier.com/retrieve/pii/S0965856408001900
  39. Santos, A., McGuckin, N., Nakamoto, H.Y., Gray, D., Liss, S.: Summary of travel trends: 2009 national household travel survey. Tech. Rep. (2011)Google Scholar
  40. Schneider, R.J.: Theory of routine mode choice decisions: an operational framework to increase sustainable transportation. Transp. Policy 25, 128–137 (2013)CrossRefGoogle Scholar
  41. Singleton, P.A., Clifton, K.J.: Pedestrians in regional travel demand forecasting models: state-of-the-practice. In: 92nd Annual Meeting of the Transportation Research Board, Washington, DC (2013)Google Scholar
  42. Train, K.E.: Discrete Choice Methods with Simulation. Cambridge University Press, Cambridge (2009)CrossRefGoogle Scholar
  43. TRB: Achieving traffic safety goals in the united states: lessons from other nations: TRB special report 300, p. 262. Transportation Research Board of the National Academies, Committee for the Study of Traffic Safety (2011)Google Scholar
  44. Ulfarsson, G.F., Mannering, F.L.: Differences in male and female injury severities in sport-utility vehicle, minivan, pickup and passenger car accidents. Accident Anal. Prev. 36(2), 135–147 (2004)CrossRefGoogle Scholar
  45. Wardman, M., Tight, M., Page, M.: Factors influencing the propensity to cycle to work. Transp. Res. Part A Policy Pract. 41(4), 339–350 (2007). http://linkinghub.elsevier.com/retrieve/pii/S0965856406001212
  46. Washington, S.P., Karlaftis, M.G., Mannering, F.L.: Statistical and econometric methods for transportation data analysis. Chapman and Hall/CRC (2011) Google Scholar
  47. Winters, M., Davidson, G., Kao, D., Teschke, K.: Motivators and deterrents of bicycling: comparing influences on decisions to ride. Transportation 38(1), 153–168. http://link.springer.com/10.1007/s11116-010-9284-y (2010)
  48. Yang, Y., Diez-Roux, A.: Walking distance by trip purpose and population subgroups. Am. J. Prev. Med. 43(1), 11–19 (2012). http://www.sciencedirect.com/science/article/pii/S0749379712002401
  49. Yang, Y., Diez Roux, A.V., Auchincloss, A.H., Rodriguez, D.A., Brown, D.G.: A spatial agent-based model for the simulation of adults’ daily walking within a city. Am. J. Prev. Med. 40(3), 353–361 (2011). doi: 10.1016/j.amepre.2010.11.017 CrossRefGoogle Scholar
  50. Yin, L.: Assessing walkability in the city of Buffalo: application of agent-based simulation. J. Urban Plan. Dev. 166–175 (2013). http://ascelibrary.org/doi/abs/10.1061/(ASCE)UP.1943-5444.0000147

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Urban Dynamics Institute, Oak Ridge National LaboratoryOak RidgeUSA
  2. 2.Department of GeographyUniversity of TennesseeKnoxvilleUSA

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