Geomatics and Environmental Engineering, vol. 17, no. 6

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Vol. 17 No. 6 (2023): Geomatics and Environmental Engineering

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Submitted
Mar 21, 2023
Accepted
Jul 17, 2023
Published
Sep 22, 2023
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This work is licensed under a Creative Commons Attribution 4.0 International License.

Measuring Objective Walkability from Pedestrian-Level Visual Perception Using Machine Learning and GSV in Khulna, Bangladesh

https://doi.org/10.7494/geom.2023.17.6.5
Gitisree Biswas
Khulna University of Engineering & Technology (KUET), Department of Urban and Regional Planning, Khulna, Bangladesh
https://orcid.org/0000-0002-4230-1624
Tusar Kanti Roy
Khulna University of Engineering & Technology (KUET), Department of Urban and Regional Planning, Khulna, Bangladesh
https://orcid.org/0000-0001-6543-5126

Corresponding Author(s) : Gitisree Biswas

giti181biswas@gmail.com

Geomatics and Environmental Engineering, Vol. 17 No. 6 (2023): Geomatics and Environmental Engineering

Abstract

Walkability entails measuring the degree of walking activity, a non-motorized mode of active transportation crucial in fast-developing urban settings and combating sedentary lifestyles. While there has been extensive objective research focusing on factors related to the physical environment that influence walkability, there has been a comparatively limited exploration into objectively evaluating a pedestrian’s visual perception. This study in Khulna, Bangladesh, aimed to develop a novel method for objectively measuring walkability based on pedestrian-level visual perception using machine learning. In this research, ResNet, a computer vision model, analyzed 127 panoramic Google Street View images taken at 200-meter intervals from seven major roads. The model, trained with the “deeplabv3plusResnet18CamVid” algorithm, quantified five selected visual features. The results, including walkability rankings, correlation analysis, and spatial mapping, highlighted that greenery and visual enclosures significantly influenced the walkability index. However, the impact of other visual features was less distinctive due to an overall poor streetscape condition. This study bridged the gap between human perception and scientific intelligence, allowing for the evaluation of previously “unmeasurable” streetscape designs. It provides valuable insights for more human-centered planning and transportation strategies, addressing the challenges of modern urbanization and sedentary behavior.

Keywords

walkability streetscapes features visual perception machine learning Google Street View (GSV)
Biswas, G., & Roy, T. K. (2023). Measuring Objective Walkability from Pedestrian-Level Visual Perception Using Machine Learning and GSV in Khulna, Bangladesh. Geomatics and Environmental Engineering, 17(6), 5–34. https://doi.org/10.7494/geom.2023.17.6.5
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References
  1. Guthold R., Stevens G.A., Riley L.M., Bull F.C.: Worldwide trends in insufficient physical activity from 2001 to 2016: A pooled analysis of 358 population-based surveys with 1.9 million participants. The Lancet Global Health, vol. 6(10), 2018, pp. e1077–e1086. https://doi.org/10.1016/S2214-109X(18)30357-7.
  2. Alfonzo M.A.: To walk or not to walk? The hierarchy of walking needs. Environment and Behavior, vol. 37(6), 2005, pp. 808–836. https://doi.org/10.1177/0013916504274016.
  3. Litman T.: Evaluating Public Transportation Health Benefits. 2011. http://site.ebrary.com/lib/sfu/docDetail.action?docID=10534560 [access: 1.09.2019].
  4. Hallal P.C., Lee I.M.: Prescription of physical activity: An undervalued intervention. The Lancet, vol. 381(9864), 2013, pp. 356–357. https://doi.org/10.1016/S0140-6736(12)61804-2.
  5. Southworth M.: Designing the walkable city. Journal of Urban Planning and Development, vol. 131(4), 2005, pp. 246–257. https://doi.org/10.1061/(ASCE)0733-9488(2005)131:4(246).
  6. Lwin K.K., Murayama Y.: Modelling of urban green space walkability: Ecofriendly walk score calculator. Computers, Environment and Urban Systems, vol. 35(5), Sep. 2011, pp. 408–420. https://doi.org/10.1016/J.COMPENVURBSYS.2011.05.002.
  7. Imani M.: City sustainability: The influence of walkability on built environments. Transportation Research Procedia, vol. 24, 2017, pp. 97–104. https://doi.org/10.1016/j.trpro.2017.05.074.
  8. Telega A., Telega I., Bieda A.: Measuring walkability with GIS-methods overview and new approach proposal. Sustainability, vol. 13(4), 2021, 1883. https://doi.org/10.3390/su13041883.
  9. Ewing R., Handy S., Brownson R.C., Clemente O., Winston E.: Identifying and measuring urban design qualities related to walkability. Journal of Physical Activity and Health, vol. 3(1), 2006, pp. s223–s240. https://doi.org/10.1123/jpah.3.s1.s223.
  10. Ewing R., Hajrasouliha A., Neckerman K.M., Purciel-Hill M., Greene W.: Streetscape features related to pedestrian activity. Journal of Planning Education and Research, vol. 36(1), 2016, pp. 5–15. https://doi.org/10.1177/0739456X15591585.
  11. Lee C., Moudon A.V.: Physical activity and environment research in the health field: Implications for urban and transportation planning practice and research. Journal of Planning Literature, vol. 19(2), 2004, pp. 147–181. https://doi.org/10.1177/0885412204267680.
  12. Ewing R., Handy S.: Measuring the unmeasurable: Urban design qualities related to walkability. Journal of Urban Design, vol. 14(1), 2009, pp. 65–84. https://doi.org/10.1080/13574800802451155.
  13. Ewing R., Clemente O., Neckerman K.M., Purciel-Hill M., Quinn J.W., Rundle A.: Measuring Urban Design: Metrics for Livable Places. Metropolitan Planning + Design, Island Press, Washington – Covelo – London 2013. https://doi.org/10.5822/978-1-61091-209-9.
  14. Anguelov D., Dulong C., Filip D., Frueh Ch., Lafon S., Lyon R., Ogale A., Vincent L., Weaver J.: Google Street View: Capturing the world at street level. Computer, vol. 43(6), 2010, pp. 32–38. https://doi.org/10.1109/MC.2010.170.
  15. Nagata S., Nakaya T., Hanibuchi T., Amagasa S., Kikuchi H., Inoue S.: Objective scoring of streetscape walkability related to leisure walking: Statistical modeling approach with semantic segmentation of Google Street View images. Health & Place, vol. 66, 2020, 102428. https://doi.org/10.1016/j.healthplace.2020.102428.
  16. Yin L., Wang Z.: Measuring visual enclosure for street walkability: Using machine learning algorithms and Google Street View imagery. Applied Geography, vol. 76, 2016, pp. 147–153. https://doi.org/10.1016/j.apgeog.2016.09.024.
  17. Steinmetz-Wood M., Velauthapillai K., O’Brien G., Ross N.A.: Assessing the micro-scale environment using Google Street View: The Virtual Systematic Tool for Evaluating Pedestrian Streetscapes (Virtual-STEPS). BMC Public Health, vol. 19(1), 2019, 1246. https://doi.org/10.1186/s12889-019-7460-3.
  18. Li X., Santi P., Courtney T.K., Verma S.K., Ratti C.: Investigating the association between streetscapes and human walking activities using Google Street View and human trajectory data. Transactions in GIS, vol. 22(4), 2018, pp. 1029–1044. https://doi.org/10.1111/tgis.12472.
  19. Koo B.W., Guhathakurta S., Botchwey N.: How are neighborhood and street-level walkability factors associated with walking behaviors? A big data approach using street view images. Environment and Behavior, vol. 54(1), 2022, pp. 211–241. https://doi.org/10.1177/00139165211014609.
  20. Lu Y.: The association of urban greenness and walking behavior: Using Google Street View and deep learning techniques to estimate residents’ exposure to urban greenness. International Journal of Environmental Research and Public Health, vol. 15(8), 2018, 1576. https://doi.org/10.3390/ijerph15081576.
  21. Yang Y., He D., Gou Z., Wang R., Liu Y., Lu Y.: Association between street greenery and walking behavior in older adults in Hong Kong. Sustainable Cities and Society, vol. 51, 2019, 101747. https://doi.org/10.1016/j.scs.2019.101747.
  22. Ki D., Lee S.: Analyzing the effects of Green View Index of neighborhood streets on walking time using Google Street View and deep learning. Landscape and Urban Planning, vol. 205, 2021, 103920. https://doi.org/10.1016/j.landurbplan.2020.103920.
  23. Yang L., Ao Y., Ke J., Lu Y., Liang Y.: To walk or not to walk? Examining nonlinear effects of streetscape greenery on walking propensity of older adults. Journal of Transport Geography, vol. 94, 103099. https://doi.org/10.1016/j.jtrangeo.2021.103099.
  24. Yang L., Liu J., Liang Y., Lu Y., Yang H.: Spatially varying effects of street greenery on walking time of older adults. ISPRS International Journal of Geo-Information, vol. 10(9), 2021, 596. https://doi.org/10.3390/ijgi10090596.
  25. Yin L., Cheng Q., Wang Z., Shao Z.: ‘Big data’ for pedestrian volume: Exploring the use of Google Street View images for pedestrian counts. Applied Geography, vol. 63, 2015, pp. 337–345. https://doi.org/10.1016/j.apgeog.2015.07.010.
  26. Zhou H., He S., Cai Y., Wang M., Su S.: Social inequalities in neighborhood visual walkability: Using street view imagery and deep learning technologies to facilitate healthy city planning. Sustainable Cities and Society, vol. 50, 2019, 101605. https://doi.org/10.1016/j.scs.2019.101605.
  27. Fonseca F., Ribeiro P.J.G., Conticelli E., Jabbari M, Papageorgiou G., Tondelli S., Ramos R.A.R.: Built environment attributes and their influence on walkability. International Journal of Sustainable Transportation, vol. 16(7), 2022, pp. 660-679. https://doi.org/10.1080/15568318.2021.1914793.
  28. Shumi S., Zuidgeest M.H.P., Martinez J.A., Efroymson D., van Maarseveen M.F.A.M.: Understanding the relationship between walkability and quality-oflife of women garment workers in Dhaka, Bangladesh. Applied Research Quality Life, vol. 10(2), 2015, pp. 263–287. https://doi.org/10.1007/s11482-014-9312-8.
  29. Siddiqua F., Kabir S., Taher M.T.: Assessing walkability of planned and historical streetscape of urban Dhaka. AIUB Journal of Science and Engineering, vol. 16(1), 2017, pp. 19–28. https://doi.org/10.53799/ajse.v16i1.28.
  30. Al Mamun M.M., Begum A.A., Mowla Q.A.: Walkability for urban sustainability: Study of pedestrian traffic in Chittagong. Jahangirnagar University Plannind Review, vol. 16, 2018, pp. 87–102. https://doi.org/10.13140/RG.2.2.35300.63367.
  31. Nasrin S., Afifah F.: Walkability assessment tool for a developing country. IOP Conference Series: Materials Science and Engineering, vol. 527(1), 2019, 012073. https://doi.org/10.1088/1757-899X/527/1/012073.
  32. Al Noman A., Sharna F.K.: An overview of pedestrian service relevant standards and guidelines: Correlative approaches of international examples with existing policies of Bangladesh. Bangladesh Institute of Planners (BIP), November 2019, pp. 44–51.
  33. Moniruzzaman M.: Sustainable and inclusive transport development of Khulna City. Khulna City Corporation, Khulna 2013.
  34. Kowaleski-Jones L., Zick C., Smith K.R., Brown B., Hanson H., Fan J.: Walkable neighborhoods and obesity: Evaluating effects with a propensity score approach. SSM – Population Health, vol. 6, 2018, pp. 9–15. https://doi.org/10.1016/j.ssmph.2017.11.005.
  35. Leather J., Fabian H., Gota S., Mejia A.: Walkability and Pedestrian Facilities in Asian Cities: State and Issues. ADB Sustainable Development Working Paper Series, no. 17, Asian Development Bank, 2011.
  36. Blečić I., Congiu T., Fancello G., Trunfio G.A.: Planning and design support tools for walkability: A guide for urban analysts. Sustainability, vol. 12(11), 2020, 4405. https://doi.org/10.3390/su12114405.
  37. Hasan M.M., Oh J.S., Kwigizile V.: Exploring the trend of walkability measures by applying hierarchical clustering technique. Journal of Transport & Health, vol. 22, 2021, 101241. https://doi.org/10.1016/j.jth.2021.101241.
  38. Rafiemanzelat R., Emadi M.I., Kamali A.J.: City sustainability: the influence of walkability on built environments. Transportation Research Procedia, vol. 24, 2017, pp. 97–104. https://doi.org/10.1016/j.trpro.2017.05.074.
  39. Arellana J., Saltarín M., Larrañaga A.M., Alvarez V., Henao C.A.: Urban walkability considering pedestrians’ perceptions of the built environment: A 10-year review and a case study in a medium-sized city in Latin America. Transport Reviews, vol. 40(2), 2020, pp. 183–203. https://doi.org/10.1080/01441647.2019.1703842.
  40. Oreskovic N.M., Roth P., Charles S.L., Tsigaridi D., Shepherd K., Nelson K.P., Bar M.: Attributes of form in the built environment that influence perceived walkability. Journal of Architectural and Planning Research, vol. 31(3), 2014, pp. 218–232. https://www.jstor.org/stable/44114605.
  41. Zhu W., Nedovic-Budic Z., Olshansky R.B., Marti J., Gao Y., Park Y., McAuley E., Chodzko-Zajko W.: Agent-based modeling of physical activity behavior and environmental correlations: An introduction and illustration. Journal of Physical Activity and Health, vol. 10(3), 2013, pp. 309–322. https://doi.org/10.1123/jpah.10.3.309.
  42. Lotfi S., Koohsari M.J.: Neighborhood walkability in a city within a developing country. Journal of Urban Planning and Development, vol. 137(4), 2011, pp. 402–408. https://doi.org/10.1061/(asce)up.1943-5444.0000085.
  43. Fan P., Wan G., Xu L., Park H., Xie Y., Liu Y., Yue W., Chen J.: Walkability in urban landscapes: A comparative study of four large cities in China. Landscape Ecology, vol. 33(2), 2018, pp. 323–340. https://doi.org/10.1007/s10980-017-0602-z.
  44. Ramakreshnan L., Fong C.S., Sulaiman N.M., Aghamohammadi N.: Motivations and built environment factors associated with campus walkability in the tropical settings. Science of the Total Environment, vol. 749, 2020, 141457. https://doi.org/10.1016/j.scitotenv.2020.141457.
  45. Khder H.M., Mousavi S.M., Khan T.H.: Impact of street’s physical elements on walkability: A case of Mawlawi Street in Sulaymaniyah, Iraq. International Journal of Built Environment and Sustainability, vol. 3(1), 2016, pp. 18–26. https://doi.org/10.11113/ijbes.v3.n1.106.
  46. Ye Y., Zeng W., Shen Q., Zhang X., Lu Y.: The visual quality of streets: A humancentred continuous measurement based on machine learning algorithms and street view images. Environment and Planning B: Urban Analytics and City Science, vol. 46(8), 2019, pp. 1439–1457. https://doi.org/10.1177/2399808319828734.
  47. Ma X. Ma C., Wu C., Xi Y., Yang R., Peng N., Zhang C., Ren F.: Measuring human perceptions of streetscapes to better inform urban renewal: A perspective of scene semantic parsing. Cities, vol. 110, 2021, 103086. https://doi.org/10.1016/j.cities.2020.103086.
  48. Dai L., Zheng C., Dong Z., Yao Y., Wang R., Zhang X., Ren S., Zhang J., Song X., Guan Q.: Analyzing the correlation between visual space and residents’ psychology in Wuhan, China using street-view images and deep-learning technique. City and Environment Interactions, vol. 11, 2021, 100069. https://doi.org/10.1016/j.cacint.2021.100069.
  49. Tao Y., Wang Y., Wang X., Tian G., Zhang S.: Measuring the correlation between human activity density and streetscape perceptions: An analysis based on Baidu street view images in Zhengzhou, China. Land, vol. 11(3), 2022, 400. https://doi.org/10.3390/land11030400.
  50. Huang G., Yu Y., Lyu M., Sun D., Zeng Q., Bart D.: Using Google Street View panoramas to investigate the influence of urban coastal street environment on visual walkability. Environmental Research Communications, vol. 5(6), 2023, 065017. https://doi.org/10.1088/2515-7620/acdecf.
  51. Dong H.: Does walkability undermine neighbourhood safety? Journal of Urban Design, vol. 22(1), 2017, pp. 59–75. https://doi.org/10.1080/13574809.2016.1247644.
  52. Reisi M., Nadoushan M.A., Aye L.: Local walkability index: Assessing built environment influence on walking. Bulletin of Geography Socio-economic Series,, vol. 46(46), 2019, pp. 7–21. https://doi.org/10.2478/bog-2019-0031.
  53. Adkins A., Dill J., Luhr G., Neal M.: Unpacking walkability: Testing the influence of urban design features on perceptions of walking environment attractiveness. Journal of Urban Design, vol. 17(4), 2012, pp. 499–510. https://doi.org/10.1080/13574809.2012.706365.
  54. Moayedi F., Zakaria R., Bigah Y., Mustafar M., Che Puan O., Zin I.S., Klufallah M.M.A.: Conceptualising the indicators of walkability for sustainable transportation. Jurnal Teknologi, vol. 65(3), pp. 85–90, 2013. https://doi.org/10.11113/jt.v65.2151.
  55. He K., Zhang X., Ren S., Sun J.: Deep residual learning for image recognition. [in:] 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), Las Vegas, Nevada, USA, 27–30 June 2016, IEEE, Poscataway 2016, pp. 770–778. https://doi.org/10.1109/CVPR.2016.90.
  56. Badrinarayanan V., Kendall A., Cipolla R.: SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39(12), 2017, pp. 2481–2495. https://doi.org/10.1109/TPAMI.2016.2644615.
  57. Chen L.-C., Papandreou G., Kokkinos I., Murphy K., Yuille A.L.: DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40(4), 2018, pp. 834–848. https://doi.org/10.1109/TPAMI.2017.2699184.
  58. Diwan T., Anirudh G., Tembhurne J.V.: Object detection using YOLO: Challenges, architectural successors, datasets and applications. Multimedia Tools and Applications, vol. 82, 2023, pp. 9243–9275. https://doi.org/10.1007/s11042-022-13644-y.
  59. Montgomery J.: Making a city: urbanity, vitality and urban design. Journal of Urban Design, vol. 3(1), 1998, pp. 93–116. https://doi.org/10.1080/13574809808724418.
  60. Li X., Ghosh D.: Associations between body mass index and urban ‘Green’ streetscape in Cleveland, Ohio, USA. International Journal of Environmental Research and Public Health, vol. 15(10), 2018, 2186. https://doi.org/10.3390/ijerph15102186.
  61. Villeneuve P.J. et al.: Comparing the normalized difference vegetation index with the google street view measure of vegetation to assess associations between greenness, walkability, recreational physical activity, and health in Ottawa, Canada. International Journal of Environmental Research and Public Health, vol. 15(8), 2018, 1719. https://doi.org/10.3390/ijerph15081719.
  62. Dong R., Zhang Y., Zhao J.: How green are the streets within the sixth ring road of Beijing? An analysis based on tencent street view pictures and the green view index. International Journal of Environmental Research and Public Health, vol. 15(7), 2018, 1367. https://doi.org/10.3390/ijerph15071367.
  63. Zhang J., Heng C.K., Malone-Lee L.C., Hii D.J.C., Janssen P., Leung K.S., Tan B.K.: Evaluating environmental implications of density: A comparative case study on the relationship between density, urban block typology and sky exposure. Automation in Construction, vol. 22, 2012, pp. 90–101. https://doi.org/10.1016/j.autcon.2011.06.011.
  64. Tang J., Long Y.: Measuring visual quality of street space and its temporal variation: Methodology and its application in the Hutong area in Beijing. Landscape and Urban Planning, vol. 191, 2019, 103436. https://doi.org/10.1016/J.LANDURBPLAN.2018.09.015.
  65. Molaei P., Tang L., Hardie M.: Measuring walkability with street connectivity and physical activity: A case study in Iran. World, vol. 2(1), 2021, pp. 49–61. https://doi.org/10.3390/world2010004.
  66. Edirisinghe T., Hewawasam D.C.: An investigation of the relationship of streetscape visual enclosure and the pedestrian movement in selected case studies in Colombo. Journal of Engineering and Architecture, vol. 8(1), 2020, pp. 10–30. https://doi.org/10.15640/jea.v8n1a2.
  67. Imamoglu Ç.: Complexity, liking and familiarity: architecture and non-architecture turkish students’ assessments of traditional and modern house facades. Journal of Environmental Psychology, vol. 20(1), 2000, pp. 5–16. https://doi.org/10.1006/JEVP.1999.0155.
  68. Stamps A.E.: Advances in visual diversity and entropy. Environment and Planning B: Planning and Design, vol. 30(3), 2003, pp. 449–463. https://doi.org/10.1068/B12986.
  69. Tucker C., Ostwald M.J., Chalup S.K.: A method for the visual analysis of streetscape character using digital image processing. [in:] Bromberek Z. (ed.), Contexts of Architecture: Proceedings of the 38th Annual Conference of the Architectural Science Association ANZAScA and the International Building Performance Simulation Association – Australasia, Launceston, 10–12 November 2004, School of Architecture, University of Tasmania, Locked Bag, Launceston 2004, pp. 134–141.
  70. Adams M.A., Phillips C.B., Patel A., Middel A.: Training computers to see the built environment related to physical activity: Detection of microscale walkabili-
  71. ty features using computer vision. International Journal of Environmental Research and Public Health, vol. 19(8), 2022, 4548. https://doi.org/10.3390/ijerph19084548.
  72. Lichstein J.W., Simons T.R., Franzreb K.E.: Landscape effects on breeding songbird abundance in managed forests. Ecological Applications, vol. 12(3), 2002, pp. 836–857. https://doi.org/10.1890/1051-0761(2002)012[0836:LEOBSA]2.0.CO;2.
  73. Fourie P.J., Jittrapirom P., Binder R.B., Tobey M.B., Medina S.O., Maheshwari T., Yamagata Y.: Modeling and Design of Smart Mobility Systems. [in:] Yamagata Y., Yang P.P.J. (eds.), Urban Systems Design: Creating Sustainable Smart Cities in the Internet of Things Era, Elsevier, 2020, pp. 163–197. https://doi.org/10.1016/B978-0-12-816055-8.00006-3.
  74. Agarwal P., Burgard W., Spinello L.: Metric localization using Google Street View. [in:] IROS: IEEE/RSJ International Conference on Intelligent Robots and Systems: September 28-October 2, 2015, Hamburg, Germany, IEEE, Piscataway 2015, pp. 3111–3118. https://doi.org/10.1109/IROS.2015.7353807.
  75. Dhillon A., Verma G.K.: Convolutional neural network: a review of models, methodologies and applications to object detection. Progress in Artificial Intelligence, vol. 9(2), 2020, pp. 85–112. https://doi.org/10.1007/s13748-019-00203-0.
  76. Zaeemzadeh A., Rahnavard N., Shah M.: Norm-preservation: Why residual networks can become extremely deep? IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43(11), 2021, pp. 3980–3990. https://doi.org/10.1109/TPAMI.2020.2990339.
  77. Lee K.K.: Developing and implementing the Active Design Guidelines in New York City. Health & Place, vol. 18(1), 2012, pp. 5–7. https://doi.org/10.1016/j.healthplace.2011.09.009.
  78. Sarwinda D., Paradisa R. H., Bustamam A., Anggia P.: Deep learning in image classification using Residual Network (ResNet) variants for detection of colorectal cancer. Procedia Computer Science, vol. 179, 2021, pp. 423–431. https://doi.org/10.1016/j.procs.2021.01.025.
  79. Cerin E., Saelens B.E., Sallis J.F., Frank L.D.: Neighborhood environment walkability scale: Validity and development of a short form. Medicine & Science in Sports & Exercise, vol. 38(9), 2006, pp. 1682–1691. https://doi.org/10.1249/01.mss.0000227639.83607.4d.
  80. Hall C.M., Ram Y.: Walk score® and its potential contribution to the study of active transport and walkability: A critical and systematic review. Transportation Research Part D: Transport and Environment, vol. 61(part B), 2018, pp. 310–324. https://doi.org/10.1016/j.trd.2017.12.018.
  81. Efroymson D. et al.: Moving Dangerously, Moving Pleasurably: Improving Walkability in Dhaka: Using a BRT Walkability Strategy to Make Dhaka’s Transportation Infrastructure Pedestrian-Friendly. 2012. https://healthbridge.ca/dist/library/BRT_Walkability_Strategy_Summary_final.pdf.
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