IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i4p1035-d138945.html
   My bibliography  Save this article

A Path Walkability Assessment Index Model for Evaluating and Facilitating Retail Walking Using Decision-Tree-Making (DTM) Method

Author

Listed:
  • Ali Keyvanfar

    (Facultad de Arquitectura y Urbanismo, Universidad Tecnológica Equinoccial, Calle Rumipamba s/n y Bourgeois, Quito 170508, Ecuador
    MIT-UTM MSCP Program, Institute Sultan Iskandar, Universiti Teknologi Malaysia, Skudai 81310, Malaysia
    Department of Landscape Architecture, Faculty of Built Environment, Universiti Teknologi Malaysia, Skudai 81310, Johor, Malaysia
    Jacobs School of Engineering, University of California, San Diego, CA 92093, USA)

  • M. Salim Ferwati

    (Department of Architecture and Urban Planning, College of Engineering, Qatar University, Doha 2713, Qatar)

  • Arezou Shafaghat

    (MIT-UTM MSCP Program, Institute Sultan Iskandar, Universiti Teknologi Malaysia, Skudai 81310, Malaysia
    Department of Landscape Architecture, Faculty of Built Environment, Universiti Teknologi Malaysia, Skudai 81310, Johor, Malaysia)

  • Hasanuddin Lamit

    (Department of Landscape Architecture, Faculty of Built Environment, Universiti Teknologi Malaysia, Skudai 81310, Johor, Malaysia)

Abstract

Transportation is the major contributor of ever-increasing CO 2 and Greenhouse Gas emissions in cities. The ever-increasing hazardous emissions of transportation and energy consumption have persuaded transportation and urban planners to motivate people to non-motorized mode of travel, especially walking. Currently, there are several urban walkability assessment models; however, coping with a limited range of walkability assessment variables make these models not fully able to promote inclusive walkable urban neighborhoods. In this regard, this study develops the path walkability assessment (PWA) index model which evaluates and analyzes path walkability in association with the pedestrian’s decision-tree-making (DTM). The model converts the pedestrian’s DTM qualitative data to quantifiable values. This model involves ninety-two (92) physical and environmental walkability assessment variables clustered into three layers of DTM (Layer 1: features; Layer 2: Criteria; and Layer 3: Sub-Criteria), and scoped to shopping and retail type of walking. The PWA model as a global decision support tool can be applied in any neighborhood in the world, and this study implements it at Taman Universiti neighborhood in Skudai, Malaysia. The PWA model has established the walkability score index which determines the grading rate of walkability accomplishment for each walkability variable of the under-survey neighborhood. Using the PWA grading index enables urban designers to manage properly the financial resource allocation for inspiring walkability in the targeted neighborhood.

Suggested Citation

  • Ali Keyvanfar & M. Salim Ferwati & Arezou Shafaghat & Hasanuddin Lamit, 2018. "A Path Walkability Assessment Index Model for Evaluating and Facilitating Retail Walking Using Decision-Tree-Making (DTM) Method," Sustainability, MDPI, vol. 10(4), pages 1-33, March.
  • Handle: RePEc:gam:jsusta:v:10:y:2018:i:4:p:1035-:d:138945
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/4/1035/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/10/4/1035/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Crane, Randall, 1998. "Travel By Design?," University of California Transportation Center, Working Papers qt3pc4v6jj, University of California Transportation Center.
    2. Ivan Blecic & Dario Canu & Arnaldo Cecchini & Tanja Congiu & Giovanna Fancello, 2017. "Walkability and Street Intersections in Rural-Urban Fringes: A Decision Aiding Evaluation Procedure," Sustainability, MDPI, vol. 9(6), pages 1-19, May.
    3. Saelens, B.E. & Sallis, J.F. & Black, J.B. & Chen, D., 2003. "Neighborhood-Based Differences in Physical Activity: An Environment Scale Evaluation," American Journal of Public Health, American Public Health Association, vol. 93(9), pages 1552-1558.
    4. Mikalsen, R. & Wang, Y.D. & Roskilly, A.P., 2009. "A comparison of Miller and Otto cycle natural gas engines for small scale CHP applications," Applied Energy, Elsevier, vol. 86(6), pages 922-927, June.
    5. Boarnet, Marlon G., 2003. "The Built Environment and Physical Activity: Empirical Methods and Data Resource," University of California Transportation Center, Working Papers qt7mj625f0, University of California Transportation Center.
    6. Bradley Bereitschaft, 2017. "Equity in Microscale Urban Design and Walkability: A Photographic Survey of Six Pittsburgh Streetscapes," Sustainability, MDPI, vol. 9(7), pages 1-20, July.
    7. Addy, C.L. & Wilson, D.K. & Kirtland, K.A. & Ainsworth, B.E. & Sharpe, P. & Kimsey, D., 2004. "Associations of Perceived Social and Physical Environmental Supports with Physical Activity and Walking Behavior," American Journal of Public Health, American Public Health Association, vol. 94(3), pages 440-443.
    8. Crane, Randall & Crepeau, Richard, 1998. "Does Neighborhood Design Influence Travel?: Behavioral Analysis of Travel Diary and GIS Data," University of California Transportation Center, Working Papers qt4pj4s7t8, University of California Transportation Center.
    9. Ekaterina Shafray & Seiyong Kim, 2017. "A Study of Walkable Spaces with Natural Elements for Urban Regeneration: A Focus on Cases in Seoul, South Korea," Sustainability, MDPI, vol. 9(4), pages 1-20, April.
    10. Handy, Susan & Cao, Xinyu & Mokhtarian, Patricia L., 2005. "Correlation or causality between the built environment and travel behavior? Evidence from Northern California," University of California Transportation Center, Working Papers qt5b76c5kg, University of California Transportation Center.
    11. Southworth, Michael & Ben-Joseph, Eran, 2004. "Reconsidering the Cul-de-sac," University of California Transportation Center, Working Papers qt1qn0g780, University of California Transportation Center.
    12. Yi Zhang & Yuan Li & Qixing Liu & Chaoyang Li, 2014. "The Built Environment and Walking Activity of the Elderly: An Empirical Analysis in the Zhongshan Metropolitan Area, China," Sustainability, MDPI, vol. 6(2), pages 1-17, February.
    13. Boarnet, Marlon & Crane, Randall, 2001. "The influence of land use on travel behavior: specification and estimation strategies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(9), pages 823-845, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Arezou Shafaghat & Salim Ferwati & Ali Keyvanfar, 2022. "COVID-19-Adapted Multi-Functional Corniche Street Design Assessment Model: Applying Global Sensitivity Analysis (GSA) and Adaptability Analysis Methods," Sustainability, MDPI, vol. 14(17), pages 1-27, September.
    2. Justyna Patalas-Maliszewska & Hanna Łosyk & Matthias Rehm, 2022. "Decision-Tree Based Methodology Aid in Assessing the Sustainable Development of a Manufacturing Company," Sustainability, MDPI, vol. 14(10), pages 1-17, May.
    3. Ivan Blečić & Tanja Congiu & Giovanna Fancello & Giuseppe Andrea Trunfio, 2020. "Planning and Design Support Tools for Walkability: A Guide for Urban Analysts," Sustainability, MDPI, vol. 12(11), pages 1-18, May.
    4. Maria Hełdak & Sultan Sevinc Kurt Konakoglu & Banu Cicek Kurdoglu & Hande Goksal & Bogdan Przybyła & Jan K. Kazak, 2021. "The Role and Importance of a Footbridge Suspended over a Highway in the Opinion of Its Users—Trabzon (Turkey)," Land, MDPI, vol. 10(4), pages 1-18, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Faizeh Hatami & Jean-Claude Thill, 2022. "Spatiotemporal Evaluation of the Built Environment’s Impact on Commuting Duration," Sustainability, MDPI, vol. 14(12), pages 1-19, June.
    2. Tae-Hyoung Gim, 2012. "A meta-analysis of the relationship between density and travel behavior," Transportation, Springer, vol. 39(3), pages 491-519, May.
    3. Zhao, Chunli & Nielsen, Thomas Alexander Sick & Olafsson, Anton Stahl & Carstensen, Trine Agervig & Meng, Xiaoying, 2018. "Urban form, demographic and socio-economic correlates of walking, cycling, and e-biking: Evidence from eight neighborhoods in Beijing," Transport Policy, Elsevier, vol. 64(C), pages 102-112.
    4. Cao, Xinyu (Jason) & Mokhtarian, Patricia L. & Handy, Susan L., 2009. "The relationship between the built environment and nonwork travel: A case study of Northern California," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(5), pages 548-559, June.
    5. Kevin Credit & Elizabeth Mack, 2019. "Place-making and performance: The impact of walkable built environments on business performance in Phoenix and Boston," Environment and Planning B, , vol. 46(2), pages 264-285, February.
    6. Javier Asensio, 2002. "Transport Mode Choice by Commuters to Barcelona's CBD," Urban Studies, Urban Studies Journal Limited, vol. 39(10), pages 1881-1895, September.
    7. Cao, Xinyu, 2006. "The Causal Relationship between the Built Environment and Personal Travel Choice: Evidence from Northern California," University of California Transportation Center, Working Papers qt07q5p340, University of California Transportation Center.
    8. Tomás Ruiz & Rosa Arroyo & Lidón Mars & Daniel Casquero, 2018. "Effects of a Travel Behaviour Change Program on Sustainable Travel," Sustainability, MDPI, vol. 10(12), pages 1-22, December.
    9. Xinyu Cao & Patricia Mokhtarian & Susan Handy, 2007. "Do changes in neighborhood characteristics lead to changes in travel behavior? A structural equations modeling approach," Transportation, Springer, vol. 34(5), pages 535-556, September.
    10. Lara Engelfriet & Eric Koomen, 2018. "The impact of urban form on commuting in large Chinese cities," Transportation, Springer, vol. 45(5), pages 1269-1295, September.
    11. Safirova, Elena A. & Houde, Sébastien & Harrington, Winston, 2007. "Spatial Development and Energy Consumption," RFF Working Paper Series dp-07-51, Resources for the Future.
    12. Louis Merlin, 2015. "Can the built environment influence nonwork activity participation? An analysis with national data," Transportation, Springer, vol. 42(2), pages 369-387, March.
    13. Yi Zhang & Wei Wu & Yuan Li & Qixing Liu & Chaoyang Li, 2014. "Does the Built Environment Make a Difference? An Investigation of Household Vehicle Use in Zhongshan Metropolitan Area, China," Sustainability, MDPI, vol. 6(8), pages 1-21, August.
    14. Reilly, Michael & Landis, John, 2003. "The Influence of Built-Form and Land Use on Mode Choice," University of California Transportation Center, Working Papers qt46r3k871, University of California Transportation Center.
    15. Tae‐Hyoung Tommy Gim, 2021. "Quantile regression on the nonlinear relationship between land use and trip time," Papers in Regional Science, Wiley Blackwell, vol. 100(4), pages 1055-1077, August.
    16. Bento, Antonio M. & Cropper, Maureen L. & Mobarak, Ahmed Mushfiq & Vinha, Katja, 2003. "The impact of urban spatial structure on travel demand in the United States," Policy Research Working Paper Series 3007, The World Bank.
    17. Cao, XinYu, 2007. "The Causal Relationship between the Built Environment and Personal Travel Choice: Evidence from Northern California," Institute of Transportation Studies, Working Paper Series qt1n90z8h8, Institute of Transportation Studies, UC Davis.
    18. Cao, Xinyu & Mokhtarian, Patricia & Handy, Susan, 2008. "Examining The Impacts of Residential Self-Selection on Travel Behavior: Methodologies and Empirical Findings," Institute of Transportation Studies, Working Paper Series qt08x1k476, Institute of Transportation Studies, UC Davis.
    19. Clark, Thomas A., 2013. "Metropolitan density, energy efficiency and carbon emissions: Multi-attribute tradeoffs and their policy implications," Energy Policy, Elsevier, vol. 53(C), pages 413-428.
    20. Huang, Xiaoyan & (Jason) Cao, Xinyu & Yin, Jiangbin & Cao, Xiaoshu, 2019. "Can metro transit reduce driving? Evidence from Xi'an, China," Transport Policy, Elsevier, vol. 81(C), pages 350-359.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:10:y:2018:i:4:p:1035-:d:138945. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.