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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

Author

Listed:
  • H. M. Abdul Aziz

    (Urban Dynamics Institute, Oak Ridge National Laboratory)

  • Nicholas N. Nagle

    (Urban Dynamics Institute, Oak Ridge National Laboratory
    University of Tennessee)

  • April M. Morton

    (Urban Dynamics Institute, Oak Ridge National Laboratory)

  • Michael R. Hilliard

    (Urban Dynamics Institute, Oak Ridge National Laboratory)

  • Devin A. White

    (Urban Dynamics Institute, Oak Ridge National Laboratory)

  • Robert N. Stewart

    (Urban Dynamics Institute, Oak Ridge National Laboratory)

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.

Suggested Citation

  • H. M. Abdul Aziz & Nicholas N. Nagle & April M. Morton & Michael R. Hilliard & Devin A. White & Robert N. Stewart, 2018. "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," Transportation, Springer, vol. 45(5), pages 1207-1229, September.
    • Handle: RePEc:kap:transp:v:45:y:2018:i:5:d:10.1007_s11116-017-9760-8
    DOI: 10.1007/s11116-017-9760-8
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    References listed on IDEAS

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    2. Yang, Binyu & Tian, Yuan & Wang, Jian & Hu, Xiaowei & An, Shi, 2022. "How to improve urban transportation planning in big data era? A practice in the study of traffic analysis zone delineation," Transport Policy, Elsevier, vol. 127(C), pages 1-14.
    3. Zhang, Xiang & Li, Wence, 2023. "Effects of a bike sharing system and COVID-19 on low-carbon traffic modal shift and emission reduction," Transport Policy, Elsevier, vol. 132(C), pages 42-64.
    4. Yuanyuan Guo & Linchuan Yang & Wenke Huang & Yi Guo, 2020. "Traffic Safety Perception, Attitude, and Feeder Mode Choice of Metro Commute: Evidence from Shenzhen," IJERPH, MDPI, vol. 17(24), pages 1-20, December.
    5. Gan, Zuoxian & Yang, Min & Zeng, Qingcheng & Timmermans, Harry J.P., 2021. "Associations between built environment, perceived walkability/bikeability and metro transfer patterns," Transportation Research Part A: Policy and Practice, Elsevier, vol. 153(C), pages 171-187.
    6. Yunwen Chen & Binhui Wang & Jing Huang & Hei Gao & Xianfan Shu, 2024. "Urban Physical Environments Promoting Active Leisure Travel: An Empirical Study Using Crowdsourced GPS Tracks and Geographic Big Data from Multiple Sources," Land, MDPI, vol. 13(5), pages 1-20, April.
    7. Eldeeb, Gamal & Mohamed, Moataz & Páez, Antonio, 2021. "Built for active travel? Investigating the contextual effects of the built environment on transportation mode choice," Journal of Transport Geography, Elsevier, vol. 96(C).
    8. Liu, Jixiang & Wang, Bo & Xiao, Longzhu, 2021. "Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach," Journal of Transport Geography, Elsevier, vol. 92(C).
    9. Jurgis Zagorskas & Marija Burinskienė, 2019. "Challenges Caused by Increased Use of E-Powered Personal Mobility Vehicles in European Cities," Sustainability, MDPI, vol. 12(1), pages 1-13, December.
    10. Yi-Wen Kuo & Cheng-Hsien Hsieh & Yu-Chen Hung, 2021. "Non-linear characteristics in switching intention to use a docked bike-sharing system," Transportation, Springer, vol. 48(3), pages 1459-1479, June.
    11. Justin B Hollander & Giorgi Nikolaishvili & Alphonsus A Adu-Bredu & Minyu Situ & Shabnam Bista, 2021. "Using deep learning to examine the correlation between transportation planning and perceived safety of the built environment," Environment and Planning B, , vol. 48(7), pages 2023-2038, September.
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