, 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 AzizEmail author
  • Nicholas N. Nagle
  • April M. Morton
  • Michael R. Hilliard
  • Devin A. White
  • Robert N. Stewart


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.


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



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 (


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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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