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Examining the relationship between active travel, weather, and the built environment: a multilevel approach using a GPS-enhanced dataset

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  • Andrew Clark
  • Darren Scott
  • Nikolaos Yiannakoulias

Abstract

This study examines how the built environment and weather conditions influence the use of walking as a mode of transport. The Halifax Regional Municipality in Nova Scotia, Canada is the study area for this work. Data are derived from three sources: a socio-demographic questionnaire and a GPS-enhanced prompted recall time-use diary collected between April 2007 and May 2008 as part of the Halifax Space-Time Activity Research project, a daily meteorological summary from Environment Canada, and a comprehensive GIS dataset from the regional municipality. Two binary logit multilevel models are estimated to examine how the propensity to use walking is influenced by the built environment and weather while controlling for socio-demographic characteristics. The built environment is measured via five attributes in one model and a walkability index (derived from the five attributes) in the other. Weather conditions are shown to affect walking use in both models. Although the walkability index is significant, the results demonstrate that this significance is driven by specific attributes of the built environment—in the case of this study, population density and to a lesser extent, pedestrian infrastructure. Copyright Springer Science+Business Media New York 2014

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  • Andrew Clark & Darren Scott & Nikolaos Yiannakoulias, 2014. "Examining the relationship between active travel, weather, and the built environment: a multilevel approach using a GPS-enhanced dataset," Transportation, Springer, vol. 41(2), pages 325-338, March.
    • Handle: RePEc:kap:transp:v:41:y:2014:i:2:p:325-338
    DOI: 10.1007/s11116-013-9476-3
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    References listed on IDEAS

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    1. Millward, Hugh & Spinney, Jamie, 2011. "Time use, travel behavior, and the rural–urban continuum: results from the Halifax STAR project," Journal of Transport Geography, Elsevier, vol. 19(1), pages 51-58.
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    3. Feuillet, T. & Commenges, H. & Menai, M. & Salze, P. & Perchoux, C. & Reuillon, R. & Kesse-Guyot, E. & Enaux, C. & Nazare, J.-A. & Hercberg, S. & Simon, C. & Charreire, H. & Oppert, J.M., 2018. "A massive geographically weighted regression model of walking-environment relationships," Journal of Transport Geography, Elsevier, vol. 68(C), pages 118-129.
    4. Fernando Fonseca & Escolástica Fernandes & Rui Ramos, 2022. "Walkable Cities: Using the Smart Pedestrian Net Method for Evaluating a Pedestrian Network in Guimarães, Portugal," Sustainability, MDPI, vol. 14(16), pages 1-23, August.
    5. Hyungkyoo Kim & Elizabeth Macdonald, 2016. "Does Wind Discourage Sustainable Transportation Mode Choice? Findings from San Francisco, California, USA," Sustainability, MDPI, vol. 8(3), pages 1-15, March.
    6. Steven R. Gehrke & Timothy F. Welch, 2017. "The built environment determinants of activity participation and walking near the workplace," Transportation, Springer, vol. 44(5), pages 941-956, September.
    7. Zuopeng Xiao & James H. Lenzer & Yanwei Chai, 2017. "Examining The Uneven Distribution Of Household Travel Carbon Emissions Within And Across Neighborhoods: The Case Of Beijing," Journal of Regional Science, Wiley Blackwell, vol. 57(3), pages 487-506, June.
    8. Arellana, Julián & Alvarez, Vilma & Oviedo, Daniel & Guzman, Luis A., 2021. "Walk this way: Pedestrian accessibility and equity in Barranquilla and Soledad, Colombia," Research in Transportation Economics, Elsevier, vol. 86(C).
    9. Tijana Đorđević & Nemanja Tomić & Dajana Tešić, 2023. "Walkability and Bikeability for Sustainable Spatial Planning in the City of Novi Sad (Serbia)," Sustainability, MDPI, vol. 15(4), pages 1-25, February.
    10. Tae-Hyoung Tommy Gim, 2017. "Full Random Coefficients Multilevel Modeling of the Relationship between Land Use and Trip Time on Weekdays and Weekends," Sustainability, MDPI, vol. 9(10), pages 1-26, October.
    11. Ferrer, Sheila & Ruiz, Tomás, 2018. "The impact of the built environment on the decision to walk for short trips: Evidence from two Spanish cities," Transport Policy, Elsevier, vol. 67(C), pages 111-120.
    12. Panyu Tang & Mahdi Aghaabbasi & Mujahid Ali & Amin Jan & Abdeliazim Mustafa Mohamed & Abdullah Mohamed, 2022. "How Sustainable Is People’s Travel to Reach Public Transit Stations to Go to Work? A Machine Learning Approach to Reveal Complex Relationships," Sustainability, MDPI, vol. 14(7), pages 1-18, March.
    13. 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).
    14. Choobchian, Pooria & Mohammadi, Ali & Zou, Bo & Hair, Joseph F. & Valinejad, Mahsa & Shin, Jaeyong & Sriraj, P.S., 2024. "Calibrating walkability indicators for commute walk trips: A structural equation modeling approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
    15. Peiravian, Farideddin & Derrible, Sybil & Ijaz, Farukh, 2014. "Development and application of the Pedestrian Environment Index (PEI)," Journal of Transport Geography, Elsevier, vol. 39(C), pages 73-84.

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