Using best–worst scaling to identify barriers to walkability: a study of Porto Alegre, Brazil

Abstract

This paper pursues three goals: (1) determining the relative importance of built environment barriers limiting walkability, (2) analyzing the existence of an asymmetry in the way people evaluate positive and negative built environment characteristics, and (3) identifying solutions to tackle the main barriers and quantify their impact in walkability. A best–worst scaling survey was developed to compare the importance of eight different attributes of the built environment regarding walkability. Model results show an asymmetry negative–positive in the judgment and choice of built environment characteristics that promote and impede walkability. The most important barriers, obtained from worst responses, are connectivity, topography, sidewalk surface and absence of policemen. Walkability scores were computed for different neighbourhoods and different policy scenarios were forecasted. Simulation results from the worst responses indicate that improvements in sidewalk quality, along with an increase in the number of police officers, lead to an 85% increase in the walkability score for the lower income neighbourhoods.

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References

  1. Abele, A.: Thinking about thinking: causal, evaluative, and finalistic congnitions about social situations. Eur. J. Soc. Psychol. 15, 315–332 (1985)

    Google Scholar 

  2. Adachi-Mejia, A.M., Drake, K.M., MacKenzie, T.A., Titus-Ernstoff, L., Longacre, M.R., Hendricks, K.M., Beach, M.L., Dalton, M.A.: Perceived intrinsic barriers to physical activity among rural mothers. J. Womens Health 19, 2197–2202 (2010)

    Google Scholar 

  3. Adamsen, J.M., Whitty, J.A.: Best-Worst scaling reflections on presentation, analysis, and lessons learnt from case 3. Mark. Soc. Res 21(1), 9–27 (2013)

    Google Scholar 

  4. Alfonzo, M.A.: To walk or not to walk? The hierarchy of walking needs. Environ. Behav. 37(6), 808–836 (2005). https://doi.org/10.1177/0013916504274016

    Article  Google Scholar 

  5. Allen, G., Dempsey, N.: Police service strength. Briefing paper 634. London: House of Commons Library http://researchbriefings.files.parliament.uk/documents/SN00634/SN00634.pdf (2017). Accessed 1 June 2017

  6. Asadi-Shekari, Z., Moeinaddini, M., Shah, M.Z.: Non-motorised level of service: addressing challenges in pedestrian and bicycle level of service. Transp. Rev. 33(2), 166–194 (2013). https://doi.org/10.1080/01441647.2013.775613

    Article  Google Scholar 

  7. Asadi-Shekari, Z., Moeinaddini, M., Shah, M.Z.: Pedestrian safety index for evaluating street facilities in urban areas. Saf. Sci. 74, 1–14 (2015)

    Google Scholar 

  8. Atlas for Human Development in Brazil: http://www.atlasbrasil.org.br/2013/pt/perfil_m/porto-alegre_rs (2013). Accessed 15 Sept 2018

  9. Badland, H.M., Opit, S., Witten, K., Kearns, R.A., Mavoa, S.: Can virtual streetscape audits reliably replace physical streetscape audits. J. Urban Health 87(6), 1007–1016 (2010). https://doi.org/10.1007/s11524-010-9505-x

    Article  Google Scholar 

  10. Badland, H., White, M., MacAulay, G., Eagleson, S., Mavoa, S., Pettit, C., Giles-Corti, B.: Using simple agent-based modeling to inform and enhance neighborhood walkability. Int. J. Health Geogr. 12, 10 (2013). https://doi.org/10.1186/1476-072x-12-58

    Article  Google Scholar 

  11. Balbontin, C., de Ortúzar, J.D., Swait, J.: A joint best–worst scaling and stated choice model considering observed and unobserved heterogeneity: an application to residential location choice. J. Choice Modell. 16(c), 1–14 (2015)

    Google Scholar 

  12. Baran, P., Rodríguez, D., Khattak, A.: Space syntax and walking in a new urbanist and suburban neighborhoods. J. Urban Des. 13(1), 5–28 (2008)

    Google Scholar 

  13. Baumeister, R.F., Bratslavsky, E., Finkenauer, C., Vohs, K.D.: Bad is stronger than good. Rev. Gen. Psychol. 5, 323–370 (2001)

    Google Scholar 

  14. Beiler, M.R.O., Phillips, B.: Prioritizing pedestrian corridors using walkability performance metrics and decision analysis. J. Urban Plan. Dev. 142(1), 12 (2016). https://doi.org/10.1061/(asce)up.1943-5444.0000290

    Article  Google Scholar 

  15. Ben-Akiva, M., Lerman, S.R.: Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press, Cambridge (1985)

    Google Scholar 

  16. Blecic, I., Cecchini, A., Congiu, T., Fancello, G., Trunfio, G.A.: Evaluating walkability: a capability-wise planning and design support system. Int. J. Geogr. Inf. Sci. 29(8), 1350–1374 (2015). https://doi.org/10.1080/13658816.2015.1026824

    Article  Google Scholar 

  17. Bostyn, D.H., Roets, A.: The morality of action: the asymmetry between judgments of praise and blame in the action–omission effect. J. Exp. Soc. Psychol. 63, 19–25 (2016)

    Google Scholar 

  18. Brebbia, C.A., Ricci, S.: Urban Transport XXIII. Wit Press, Southampton (2017)

    Google Scholar 

  19. Brownson, R.C., Hoehner, C.M., Day, K., Forsyth, A., Sallis, J.F.: Measuring the built environment for physical activity: state of the science. Am. J. Prev. Med. 36, S99-123.e12 (2009). https://doi.org/10.1016/j.amepre.2009.01.005

    Article  Google Scholar 

  20. Burden, D.: Building communities with transportation. Transp. Res. Rec. 1773, 5–20 (2001)

    Google Scholar 

  21. Burge, P., Potoglou, D., Flynn, T., Brazier, J.E., Netten, A.: Bestworst scaling: Consistency of preferences with discrete choice experiments and stability over time. In: International Choice Modelling Conference, Leeds (2011)

  22. Burke, P.F, Louviere, J., Wei, E., MacAulay, G., Quail, K., Carson, R.: Overcoming Challenges and Improvements in Best-Worst Elicitation: Determining What Matters to Japanese Wheat Millers. Open Conference Systems. http://www.opus.lib.uts.edu.au (2013). Accessed 03 Feb 2015

  23. Cao, X., Handy, S., Mokhtarian, P.: The influences of the built environment and residential self-selection on pedestrian behavior, Tx. Transportation 33(1), 1–20 (2006)

    Google Scholar 

  24. Cao, X., Mokhtarian, P.L., Handy, S.L.: Examining the impacts of residential self-selection on travel behaviour: a focus on empirical findings. Transp. Rev. 29(3), 359–395 (2009). https://doi.org/10.1080/01441640802539195

    Article  Google Scholar 

  25. Cerin, E., Saelens, B.E., Sallis, J.F., Frank, L.D.: Neighborhood environment walkability scale: validity and development of a short form. Med. Sci. Sports Exerc. 38(9), 1682–1691 (2006)

    Google Scholar 

  26. Cerin, E., Leslie, E., Owen, N.: Explaining socio-economic status differences in walking for transport: an ecological analysis of individual, social and environmental factors. Soc. Sci. Med. 68(6), 1013–1020 (2009). https://doi.org/10.1016/j.socscimed.2009.01.008

    Article  Google Scholar 

  27. Cervero, R., Duncan, M.: Walking, bicycling, and urban landscapes: evidence from the San Francisco Bay Area. Am. J. Public Health 93(9), 1478–1483 (2003)

    Google Scholar 

  28. Cervero, R., Kockelman, K.: Travel demand and the 3Ds: density, diversity, and design. Transp. Res. D 2(3), 199–219 (1997)

    Google Scholar 

  29. Cervero, R., Sarmiento, O.L., Jacoby, E., Gomez, L.F., e Neiman, A.: Influences of built environments on walking and cycling: lessons from Bogotá. Int. J. Sustain. Transp. 3(4), 203–226 (2009)

    Google Scholar 

  30. Cheung, C.M.K., Lee, M.K.O.: Positive–negative asymmetry of disconfirmations on user satisfaction judgment. In: Pre-ICIS Workshop, IS-Core, LasVegas, NV, December. Vol. 11. https://pdfs.semanticscholar.org/84f5/6e50e66cf887e05cc62eb6e01416d4fdd247.pdf (2005). Accessed 25 June 2017

  31. Chiang, Y.-C., Lei, H.-Y.: Using expert decision-making to establish indicators of urban friendliness for walking environments: a multidisciplinary assessment. Int. J. Health Geogr. 15, 40 (2016)

    Google Scholar 

  32. Choice Metrics: Ngene 1.1 User Manual and Reference Guide. Choice Metrics. https://www.choice-metrics.com/documentation.html (2013). Accessed 10 Dec 2017

  33. Christopoulou, P., Pitsiava-Latinopoulou, M.: Development of a model for the estimation of pedestrian level of service in Greek urban areas. Procedia Soc. Behav. Sci. 48, 1691–1701 (2012)

    Google Scholar 

  34. Clark, A.F., Scott, D.M.: Article barriers to walking: an investigation of adults in Hamilton (Ontario, Canada). Int. J. Environ. Res. Public Health (2016). https://doi.org/10.3390/ijerph13020179

    Article  Google Scholar 

  35. Clarke, P., Ailshire, J., Melendez, R., Bader, M.D.M., Morenoff, J.: Using google earth to conduct a neighborhood audit: reliability of a virtual audit instrument. Health Place 16(6), 1224–1229 (2010). https://doi.org/10.1016/j.healthplace.20

    Article  Google Scholar 

  36. Coltman, T.R., Devinney, T.M., Keating, B.W.: Best–worst scaling approach to predict customer choice for 3PL services. J. Bus. Logist. 32(2), 139–152 (2011)

    Google Scholar 

  37. Criterion Planners Engineers: INDEX PlanBuilder Users Guide, Portland, OR (2001)

  38. Nationmaster: http://www.nationmaster.com/ (2017). Accessed 05 Mar 2017

  39. Craig, C.L., Brownson, R.C., Cragg, S.E., Dunn, A.L.: Exploring the effect of the environment on physical activity: a study examining walking to work. Am. J. Prev. Med. 23, 36–43 (2002)

    Google Scholar 

  40. Daly, A., Dekker, T., Hess, S.: Dummy coding versus effects coding for categorical variables: clarifications and extensions. J. Choice Modell. 21, 36–41 (2016)

    Google Scholar 

  41. Dandan, T.A.N., Wei, W., Jian, L.U., Yang, B.: Research on methods of assessing pedestrian level of service for sidewalk. J. Transp. Syst. Eng. Inf. Technol. 7(5), 74–79 (2007)

    Google Scholar 

  42. Daniel, B.D., Nor, S.N.M., Rohani, M.M., Prasetijo, J., Aman, M.Y., Ambak, K.: Pedestrian Footpath Level of Service (FOOT-LOS) Model for Johor Bahru (2016)

  43. Department of Public Safety: Indicadores Criminais. http://www.ssp.rs.gov.br/indicadores-criminais (2018). Accessed 15 Sept 2018

  44. Dias, J.A., Dias, J.G., Lages, C.: Can negative characters in soap operas be positive for product placement? J. Bus. Res. 71, 125–132 (2017)

    Google Scholar 

  45. Diener, E., Larsen, R.J., Levine, S., Emmons, R.A.: Intensity and frequency: dimensions underlying positive and negative affect. J. Pers. Soc. Psychol. 48, 1253–1265 (1985)

    Google Scholar 

  46. Dill, J.: Measuring network connectivity for bicycling and walking. In: Transport Research Board Annual Meeting. Washington DC: Transportation Research Board 2004 (CD-ROM) http://reconnectingamerica.org/assets/Uploads/TRB2004-001550.pdf (2004)

  47. Dixon, L.: Bicycle and pedestrian level-of-service performance measures and standards for congestion management systems. Transp. Res. Rec. J. Transp. Res. Board 1538, 1–9 (1996)

    Google Scholar 

  48. Dobesova, Z., Krivka, T.: Walkability Index in the Urban Planning: A Case Study in Olomouc City. INTECH Open Access Publisher, Rijeka (2012)

    Google Scholar 

  49. Dowling, R., Flannery, A., Landis, B., Petritsch, T., Rouphail, N., Ryus, P.: Multimodal level of service for urban streets. Transp. Res. Rec. 2071, 1–7 (2008). https://doi.org/10.3141/2071-01

    Article  Google Scholar 

  50. Elias, A.: Automobile-oriented or complete street? pedestrian and bicycle level of service in the new multimodal paradigm. Transp. Res. Rec. 2257, 80–86 (2011). https://doi.org/10.3141/2257-09

    Article  Google Scholar 

  51. Ellis, G., Hunter, R., Tully, M.A., Donnelly, M., Kelleher, L., Kee, F.: Connectivity and physical activity: using footpath networks to measure the walkability of built environments. Environ. Plan. B Plan. Des. 43(1), 130–151 (2016). https://doi.org/10.1177/0265813515610672

    Article  Google Scholar 

  52. Emery, J., Crump, C., Bors, P.: Reliability and validity of two instruments designed to assess the walking and bicycling suitability of sidewalks and roads. Am. J. Health Promot. 18(1), 38–46 (2003)

    Google Scholar 

  53. Ewing, R., Cervero, R.: Travel and the built environment—a meta-analysis. J. Am. Plan. Assoc. 76, 265–294 (2010)

    Google Scholar 

  54. Ewing, R., Handy, S.: Measuring the unmeasurable: urban design qualities related to walkability. J. Urban Des. 14(1), 65–84 (2009). https://doi.org/10.1080/13574800802451155

    Article  Google Scholar 

  55. Feldman, G., Wong, K.F.E., Baumeister, R.F.: Bad is freer than good: positive–negative asymmetry in attributions of free will. Conscious. Cogn. 42, 26–40 (2016)

    Google Scholar 

  56. Flynn, T.N.: Valuing citizen and patient preferences in health: recent developments in three types of best–worst scaling. Expert Rev. Pharmacoecon. Outcomes Res. 10(3), 259–267 (2010a). https://doi.org/10.1586/erp.10.29

    Article  Google Scholar 

  57. Flynn, T.N.: Using conjoint analysis and choice experiments to estimate quality adjusted life year values: issues to consider. Pharmacoeconomics 28, 711–722 (2010b)

    Google Scholar 

  58. Forsyth, A., Hearst, M., Oakes, J.M., Schmitz, K.H.: Design and destinations: factors influencing walking and total physical activity. Urb. Stud. 45(9), 1973–1996 (2008). https://doi.org/10.1177/0042098008093386

    Article  Google Scholar 

  59. Foster, S., Giles-Corti, B.: The built environment, neighborhood crime and constrained physical activity: an exploration of inconsistent findings. Prev. Med. 47(3), 241–251 (2012)

    Google Scholar 

  60. Frackelton, A., Grossman, A., Palinginis, E., Castrillon, F., Elango, V., Guensler, R.: Measuring walkability: development of an automated sidewalk quality assessment tool. Suburban Sustain. 1(1), 4 (2013)

    Google Scholar 

  61. Frank, L., Engelke, P.: The built environment and human activity patterns: exploring the impacts of urban for mon public health. J. Plan. Lit. 16, 202–218 (2001)

    Google Scholar 

  62. Frank, L.D., Schmid, T.L., Sallis, J.F., Chapman, J., Saelens, B.E.: Linking objectively measured physical activity with objectively measured urban form—Findings from SMARTRAQ. Am. J. Prev. Med. 28(2), 117–125 (2005). https://doi.org/10.1016/j.amepre.2004.11.001

    Article  Google Scholar 

  63. Frank, L.D., Sallis, J.F., Conway, T.L., Chapman, J.E., Saelens, B.E., Bachman, W.: Many pathways from land use to health—associations between neighborhood walkability and active transportation, body mass index, and air quality. J. Am. Plan. Assoc. 72(1), 75–87 (2006). https://doi.org/10.1080/01944360608976725

    Article  Google Scholar 

  64. Frank, L., Kerr, J., Chapman, J., Sallis, J.: Urban form relationships with walk trip frequency and distance among youth. Am. J. Health Promot. AJHP 21(4), 305–311 (2007). https://doi.org/10.4278/0890-1171-21.4s.305

    Article  Google Scholar 

  65. Gallin, N.: Quantifying pedestrian friendliness–guidelines for assessing pedestrian level of service. Road Transp. Res. 10(1), 47 (2001)

    Google Scholar 

  66. Giles-Corti, B., Donovan, R.J.: Socioeconomic status differences in recreational physical activity levels and real and perceived access to a supportive physical environment. Prev. Med. 35, 601–611 (2002)

    Google Scholar 

  67. Glazier, R.H., Weyman, J.T., Creatore, M.I., Gozdyra, P., Moineddin, R., Matheson, F.I., Booth, G.L.: Development and validation of an urban walkability index for Toronto, Canada. Can. J. Diabetes 32(4) (2008)

  68. Google Maps: Map of Porto Alegre. Google. https://www.google.com.br/maps/@-30.0415024,-51.2212028,13z (2017). Accessed 01 Mar 2017

  69. Gori, S., Nigro, M., Petrelli, M.: Walkability indicators for pedestrian-friendly design. Transp. Res. Rec. (2464), 38–45. (2014). https://doi.org/10.3141/2464-05

    Google Scholar 

  70. Graziano, W.G., Brothen, T., Berscheid, E.: Attention, attraction, and individual differences in reaction to criticism. J. Pers. Soc. Psychol. 38, 193–202 (1980)

    Google Scholar 

  71. Greenwald, M.J., Boarnet, M.G.: Built environment as determinant of walking behavior: analyzing nonwork pedestrian travel in Portland Oregon. Transp. Res. Rec. J. Transp. Res. Board 1780(1), 33–41 (2001). https://doi.org/10.3141/1780-05

    Article  Google Scholar 

  72. Griew, P., Hillsdon, M., Foster, C., Coombes, E., Jones, A., Wilkinson, P.: Developing and testing a street audit tool using Google Street View to measure environmental supportiveness for physical activity. Int. J. Behav. Nutr. Phys. Act. 10, 110 (2013)

    Google Scholar 

  73. Gullón, P., Badland, H., Alfayate, S., Bilal, U., Escobar, F., Cebrecos, A., et al.: Assessing walking and cycling environments in the streets of Madrid: comparing on-field and virtual audits. J. Urban Health 92(5), 923–939 (2015)

    Google Scholar 

  74. Guttenplan, M., Davis, B., Steiner, R., Miller, D., Trb: Planning-level areawide multimodal level-of-service analysis—performance measures for congestion management. Transp. Plan. Anal. Plan. Admin. (1858), 61–68 (2003)

  75. Guttenplan, M., Landis, B. W., Crider, L., McLeod, D. S., & Trb. (2001). Multimodal level-of-service analysis at planning level. Traffic Flow Theory Highw. Capacit. Highw. Oper. Capacit. Traffic Control (1776), 151–158

  76. Ha, E., Joo, Y., Jun, C.: An empirical study on sustainable walkability indices for transit-oriented development by using the analytic network process approach. Int. J. Urban Sci. 15(2), 137–146 (2011)

    Google Scholar 

  77. Hall, R.A.: HPE’s Walkability index–quantifying the pedestrian experience. In: Transport Research Board Annual Meeting. Transportation Research Board 210(CD-ROM), Washington DC (2010)

  78. Hamilton, D.L., Zanna, M.P.: Differential weighting of favorable and unfavorable attributes in impressions of personality. J. Exp. Res. Personal. 6, 204–212 (1972)

    Google Scholar 

  79. Handy, S.L., Clifton, K.J.: Local shopping as a strategy for reducing automobile travel. Transportation 28, 317–346 (2001)

    Google Scholar 

  80. Herr, P.M., Page, C.M., Pfeiffer, B.E., Davis, D.F.: Affective influences on evaluative processing. J. Consum. Res. 38, 833–845 (2012)

    Google Scholar 

  81. Herrmann, B., Ross, E.-G.: In: Transportation Research Board 96th Annual 35 Meeting in Washington, DC, TRB 2017 Annual Meeting

  82. Hoehner, C.M., Ramirez, L.K.B., Elliott, M.B., Handy, S.L., Brownson, R.C.: Perceived and objective environmental measures and physical activity among urban adults. Am. J. Prev. Med. 28(2), 105–116 (2005). https://doi.org/10.1016/j.amepre.2004.10.023

    Article  Google Scholar 

  83. Hong, J., Chen, C.: The role of the built environment on perceived safety from crime and walking: examining direct and indirect impacts. Transportation 41, 1171 (2014). https://doi.org/10.1007/s11116-014-9535-4

    Article  Google Scholar 

  84. Hsee, C.K., Rottenstreich, Y., Tang, J.: Asymmetries between positives and negatives. Soc. Pers. Psychol. Compass 8, 699–707 (2014)

    Google Scholar 

  85. Iachan, R.: Systematic sampling: a critical review. Int. Stat. Rev. Revue Internationale De Statistique 50(3), 293–303 (1982). https://doi.org/10.2307/1402499

    Article  Google Scholar 

  86. Iacono, M., Krizek, K.J., El-Geneidy, A.: Measuring non-motorized accessibility: issues, alternatives, and execution. J. Transp. Geogr. 18(1), 133–140 (2010). https://doi.org/10.1016/j.jtrangeo.2009.02.002

    Article  Google Scholar 

  87. IBGE (Brazilian National Census): http://www.censo2010.ibge.gov.br/en/ (2010)

  88. Ikegami, T.: Positive-negative asymmetry of priming effects on impression formation. Eur. J. Soc. Psychol. 23, 1–16 (1993)

    Google Scholar 

  89. Jaskiewicz, F.: Pedestrian level of service based on trip quality. Transportation Research Circular, TRB (2000)

  90. Jensen, S.U.: Pedestrian and bicyclist level of service on roadway segments. Transp. Res. Rec. (2031), 43–51 (2007). https://doi.org/10.3141/2031-06

    Google Scholar 

  91. Kahneman, D., Tversky, A.: Prospect theory: an analysis of decision under risk. Econometrica 47(2), 263–291 (1979)

    Google Scholar 

  92. Kamargianni, M., Polydoropoulou, A., Goulias, K.G.: Teenager’s travel patterns for school and after-school activities. Procedia Soc. Behav. Sci. 48, 3635–3650 (2013)

    Google Scholar 

  93. Kaparias, I., Bell, M.G.H., Miri, A., Chan, C., Mount, B.: Analysing the perceptions of pedestrians and drivers to shared space. Transp. Res. Part F Traffic Psychol. Behav. 15(3), 297–310 (2012)

    Google Scholar 

  94. Kelly, C.E., Tight, M.R., Hodgson, F.C., Page, M.W.: A comparison of three methods for assessing the walkability of the pedestrian environment. J. Transp. Geogr. 19(6), 1500–1508 (2011). https://doi.org/10.1016/j.jtrangeo.2010.08.001

    Article  Google Scholar 

  95. Khan, M., Kockelman, K.M., Xiong, X.: Models for anticipating non-motorized travel choices, and the role of the built environment. Transp. Policy 35, 117–126 (2014). https://doi.org/10.1016/j.tranpol.2014.05.008

    Article  Google Scholar 

  96. Kim, T.-H., Park, J.-t., Lim, J.-h., Joo, Y.: A development of integrated evaluation criteria for quality of service on pedestrian networks by using multi-criteria decision analysis (2009). https://doi.org/10.22260/ISARC2011/0112

  97. Kim, S., Choi, J., Kim, S.: Roadside walking environments and major factors affecting pedestrian level of service. Int. J. Urban Sci. 17(3), 304–315 (2013)

    Google Scholar 

  98. Kim, S., Park, S., Lee, J.S.: Meso-or micro-scale? Environmental factors influencing pedestrian satisfaction. Transp. Res. Part D: Transp. Environ. 30, 10–20 (2014)

    Google Scholar 

  99. Klinger, E., Barta, S.G., Maxeiner, M.E.: Motivational correlates of thought content frequency and commitment. J. Pers. Soc. Psychol. 39, 1222–1237 (1980)

    Google Scholar 

  100. Koh, P.P., Wong, Y.D.: Influence of infrastructural compatibility factors on walking and cycling route choices. J. Environ. Psychol. 36, 202–213 (2013)

    Google Scholar 

  101. Krambeck, H.V.: The Global Walkability Index. Department of urban and planning and department of civil and environmental engineering, Massachusetts Institute of Technology. http://dspace.mit.edu/handle/1721.1/34409 (2006). Accessed 5 Nov 2017

  102. Kubat, A.S., Ozer, O., Ozbil, A.: Defining a strategical framework for urban pedestrianization projects. https://faculty.ozyegin.edu.tr/ayseo/files/2014/02/SSS9_2013.pdf (2013). Accessed Sept 2018

  103. Lamíquiz, P.J., López-Domínguez, J.: Effects of built environment on walking at the neighbourhood scale. A new role for street networks by modelling their configurational accessibility? Transp. Res. Part A Policy Pract. 74, 148–163 (2015)

    Google Scholar 

  104. Lancsar, E., Louviere, J., Flynn, T.: Several methods to investigate relative attribute impact in stated preference experiments. Soc. Sci. Med. 64(8), 1738–1753 (2007)

    Google Scholar 

  105. Landis, B.W., Vattikuti, V.R., Ottenberg, R.M., McLeod, D.S., Guttenplan, M., Trb: Modeling the roadside walking environment—pedestrian level of service. In: 2001 Trb Distinguished Lecture, Pt 1—Bicycle and Pedestrian Research, Pt 2: Safety and Human Performance, pp. 82–88. Washington: Transportation Research Board Natl Research Council (2001)

  106. Larranaga, A.M., Cybis, H.B.: The relationship between built environment and walking for different trip purposes in Porto Alegre, Brazil. Int. J. Sustain. Dev. Plan. Encourag. Unified Approach Achiev. Sustain. 9, 568–580 (2014). https://doi.org/10.2495/SDP-V9-N4-568-580

    Article  Google Scholar 

  107. Larranaga, A.M., Rizzi, L.I., Arellana, J., Strambi, O., Cybis, H.B.: The Influence of built environment and travel attitudes on walking: a case study of Porto Alegre, Brazil. Int. J. Sustain. Transp. 28, 1–5 (2014). https://doi.org/10.1080/15568318.2014.933986

    Article  Google Scholar 

  108. Law no. 12.112/16: http://dopaonlineupload.procempa.com.br/dopaonlineupload/1857_ce_172548_1.pdf (2016)

  109. Lee, C., Moudon, A.V.: Correlates of walking for transportation or recreation purposes. J. Phys. Act. Health 3(1), 77–98 (2006)

    Google Scholar 

  110. Lee, J.A., Soutar, G., Louviere, J.: The best–worst scaling approach: an alternative to Schwartz’s values survey. J. Pers. Assess. 90(4), 335–347 (2008)

    Google Scholar 

  111. Lee, S., Lee, S., Son, H., Joo, Y.: A new approach for the evaluation of the walking environment. Int. J. Sustain. Transp. 7(3), 238–260 (2013)

    Google Scholar 

  112. Leslie, E.S., Brian, S., Frank, L., Owen, N., Bauman, A., Coffee, N., Hugo, G.: Residents’ perceptions of walkability attributes in objectively different neighbourhoods: a pilot study. Health Place 11(3), 227–236 (2005)

    Google Scholar 

  113. Leslie, E., Coffee, N., Frank, L., Owen, N., Bauman, A., Hugo, G.: Walkability of local communities: using geographic information systems to objectively assess relevant environmental attributes. Health Place 13(1), 111–122 (2007). https://doi.org/10.1016/j.healthplace.2005.11.001

    Article  Google Scholar 

  114. Lindelöw, D., Svensson, Å., Brundell-Freij, K., Hiselius, L.W.: Satisfaction or compensation? The interaction between walking preferences and neighbourhood design. Transp. Res. Part D 50, 520–532 (2017)

    Google Scholar 

  115. Litman, T.: Integrating public health objectives in transportation decision- making. Am. J. Health Promot. 18(n.1), 103–108 (2003)

    Google Scholar 

  116. Loo, B.P.Y., Lam, W.W.Y.: Geographic accessibility around health care facilities for elderly residents in Hong Kong: a microscale walkability assessment. Environ. Plan. B Plan. Des. 39(4), 629–646 (2012). https://doi.org/10.1068/b36146

    Article  Google Scholar 

  117. Loukaitou-Sideris, A.: Is it safe to walk? Neighborhood safety and security considerations and their effects on walking. J. Plan. Lit. 20(3), 219–232 (2006). https://doi.org/10.1177/0885412205282770

    Article  Google Scholar 

  118. Louviere, J.J., Islam, T.: A Comparison of importance weights/measures derived from choice-based conjoint, constant sum scales and best worst scaling. J. Bus. Res. 61, 903–911 (2008)

    Google Scholar 

  119. Louviere, J., Swait, J.D.: Separating weights and scale values in conjoint tasks using choices of best and worst attribute levels. Working Paper, Centre for the Study of Choice, University of Technology Sydney (1997)

  120. Louviere, J.: Analyzing Decision Making: Metric Conjoint Analysis (Quantitative Applications in the Social Sciences). SAGE Publications, Inc; 1 edition (1988)

  121. Lovasi, G.S., Schwartz-Soicher, O., Quinn, J.W., Berger, D.K., Nickerman, K.M., Jaslow, R., Lee, K.K., Rundle, A.: Neighborhood safety and green space as predictors of obesity among preschool children from low-income families in New York City. Prevent. Med. 57(3), 189–193 (2013)

    Google Scholar 

  122. Ma, L., Mulley, C., Liu, W.: Social marketing and the built environment: what matters for travel behaviour change? Transportation 44(5), 1147–1167 (2017). https://doi.org/10.1007/s11116-016-9698-2

    Article  Google Scholar 

  123. Mantri, A.: A GIS based approach to measure walkability of a neighborhood. University of Cincinnati (2008)

  124. Marley, A.A., Louviere, J.: Some probabilistic models of best, worst, and best–worst choices. J. Math. Psychol. 49, 464–480 (2005)

    Google Scholar 

  125. Matley, T., Goldman, L., Fineman, B.: Pedestrian travel potential in Northern New Jersey: a metropolitan planning organization’s approach to identifying investment priorities. Transp. Res. Rec. J. Transp. Res. Board 1705, 1–8 (2000)

    Google Scholar 

  126. Mehta, V.: Walkable streets: pedestrian behavior, perceptions and attitudes. J. Urban. 1(3), 217–245 (2008)

    Google Scholar 

  127. Middleton, J.: ‘Stepping in Time’: walking, time, and space in the city. Environ. Plan. A 41(8), 1943–1961 (2009)

    Google Scholar 

  128. Moudon, A.V., Lee, C., Cheadle, A.D., Garvin, C., Johnson, D., Schmid, T.L., et al.: Operational definitions of walkable neighborhood: theoretical and empirical insights. J. Phys. Act. Health 3, S99 (2006)

    Google Scholar 

  129. Moura, F., Cambra, P., Gonçalves, A.B.: Measuring walkability for distinct pedestrian groups with a participatory assessment method: a case study in Lisbon. Landsc. Urban Plan. 157, 282–296.e (2017)

    Google Scholar 

  130. Muraleetharan, T., Adachi, T., Hagiwara, T., Kagaya, S.: Method to determine overall level-of service of pedestrian walkways based on total utility value. J. Infrastruct. Plann. Manage. Japan Soc. Civ. Eng. (JSCE). 22, 685–693 (2004)

    Google Scholar 

  131. Muraleetharan, T., Hagiwara, T.: Overall level of service of urban walking environment and its influence on pedestrian route choice behavior: analysis of pedestrian travel in Sapporo, Japan. Transp. Res. Rec. J. Transp. Res. Board (2007)

  132. Park, S., Deakin, E., Lee, J.S.: Perception-based walkability index to test impact of micro level walkability on sustainable mode choice decisions. Transp. Res. Rec. (2464), 126–134 (2014). https://doi.org/10.3141/2464-16

    Google Scholar 

  133. Park, S.: Defining, measuring, and evaluating path walkability, and testing its impacts on transit users’ mode choice and walking distance to the station. ProQuest (2008)

  134. Peiravian, F., Derrible, S., Ijaz, F.: Development and application of the Pedestrian Environment Index (PEI). J. Transp. Geogr. 39, 73–84 (2014)

    Google Scholar 

  135. Pikora, T.J., Giles-Corti, B., Bull, F.C.L., Jamrozik, K., Donovan, R.J.: Developing a framework for assessment of the environmental determinants of walking and cycling. Soc. Sci. Med. 56(8), 1693–1703 (2003)

    Google Scholar 

  136. Pratto, F., John, O.P.: Automatic vigilance: the attention-grabbing power of negative social information. J. Pers. Soc. Psychol. 61, 380–391 (1991)

    Google Scholar 

  137. Ramezani, S., Pizzo, B., Deakin, E.: An integrated assessment of factors affecting modal choice: towards a better understanding of the causal effects of built environment. Transportation 1–37 (2017). https://doi.org/10.1007/s11116-017-9767-1

    Google Scholar 

  138. Rietveld, P.: Biking and walking: the position of non-motorized transport modes in transport systems. In: Kenneth, B., David, H. (eds.) Handbook of transport systems and traffic control, pp. 299–320. Elsevier Science Ltd., Oxford (2001)

    Google Scholar 

  139. Rodriguez, D., Joo, J.: The relationship between non-motorized mode choice and the local physical environment. Transp. Res. Part D Transp. Environ. 9(2), 151–173 (2004)

    Google Scholar 

  140. Rose, J.M., Bliemer, M.C.: Constructing efficient stated choice experimental designs. Transp. Rev. 5(n.29), 587–617 (2009)

    Google Scholar 

  141. Ruiz-Padillo, A., Pasqual, F.M., Larranaga Uriarte, A.M., Cybis, H.B.: Application of multi-criteria decision analysis methods for assessing walkability: a case study in Porto Alegre Brazil. Transp. Res. Part D Transp. Environ. 63, 855–871 (2018). https://doi.org/10.1016/j.trd.2018.07.016

    Article  Google Scholar 

  142. Rundle, A., Bader, M.D.M., Richards, C.A., Neckerman, K.M., Teitler, J.O.: Using google street view to audit neighborhood environments. Am. J. Prev. Med. 40(1), 94–100 (2011). https://doi.org/10.1016/j.amepre.20

    Article  Google Scholar 

  143. Rutland, A., Brown, R.J., Cameron, L., Ahmavaara, A., Arnold, K., Samson, J.: Development of the positive-negative asymmetry effect: in-group exclusion norm as a mediator of children’s evaluations on negative attributes. Eur. J. Soc. Psychol. 37, 171–190 (2007)

    Google Scholar 

  144. Saelens, B.E., Sallis, J.F., Frank, L.D.: Environmental correlates of walking and cycling: findings from the transportation, urban design, and planning literatures. Ann. Behav. Med. (2003). https://doi.org/10.1207/S15324796ABM2502_03

    Google Scholar 

  145. Saelens, B.E., Handy, S.: Built environment correlates of walking: a review. Med. Sci. Sports Exerc. 40(S), 550–567 (2008)

    Google Scholar 

  146. Sawtooth Software: The CBC system for choice-based conjoint analysis: Version 8.0. Technical Paper. Orem, Utah USA https://www.sawtoothsoftware.com/download/techpap/cbctech.pdf (2013). Accessed 05 Mar 2017

  147. Sayyadi, G., Awasthi, A.: AHP-based approach for location planning of pedestrian zones: application in Montreal, Canada. J. Transp. Eng. Asce 139(2), 239–246 (2013). https://doi.org/10.1061/(asce)te.1943-5436.0000493

    Article  Google Scholar 

  148. Secretariat of Urban Mobility of Porto Alegre: Origin and destination survey of Porto Alegre: EDOM 2003 (Technical Report). Porto Alegre. http://lproweb.procempa.com.br/pmpa/prefpoa/eptc/usu_doc/relatorio_edom_2003.pdf (2004). Accessed 3 Apr 2016

  149. Sehatzadeh, B., Noland, R.B., Weiner, M.D.: Walking frequency, cars, dogs, and the built environment. Transp. Res. Part A 45, 741–754 (2011)

    Google Scholar 

  150. Sharma, V., Al-Hussein, M., Safouhi, H., Boufergubene, A.: Municipal infrastructure asset levels of service assessment for investment decisions using analytic hierarchy process. J. Infrastr. Syst. 14(3), 193–200 (2008). https://doi.org/10.1061/(asce)1076-0342(2008)14:3(193)

    Article  Google Scholar 

  151. Shatu, F., Yigitcanlar, T.: Development and validity of a virtual street walkability audit tool for pedestrian route choice analysis—SWATCH. J. Transp. Geogr. 70, 148–160 (2018). https://doi.org/10.1016/j.jtrangeo.2018.06.004

    Article  Google Scholar 

  152. Shriver, K.: Influence of environmental design on pedestrian travel behavior in four austin neighborhoods. Transp. Res. Rec. 1578. http://www.enhancements.org/download/trb/1578-09.PDF (1997). Accessed 21 Nov 2017

  153. Singleton, P.A., Wang, L.: Safety and security in discretionary travel decision making: focus on active travel mode and destination choice. Transp. Res. Rec. J. Transp. Res. Board 2430, 47–58 (2014). https://doi.org/10.3141/2430-06

    Article  Google Scholar 

  154. Skowronski, J.J., Carlston, D.E.: Caught in the act: when impressions based on highly diagnostic behaviors are resistant to contradiction. Eur. J. Soc. Psychol 22, 435–452 (1992)

    Google Scholar 

  155. Spinney, J., Millward, H.: Time and money: a new look at poverty and the barriers to physical activity in Canada. Soc. Indic. Res. 99, 341–356 (2010)

    Google Scholar 

  156. Stantec: Proposed walkability strategy for Edmonton. Stantec Consulting Ltda. Glatting Jackson Kercher Anglin, Inc. Project for Public Spaces (2010)

  157. Sung, H., Lee, S.: Residential built environment and walking activity: empirical evidence of Jane Jacobs’ urban vitality. Transp. Res. Part D Transp. Environ. 41, 318–329 (2015)

    Google Scholar 

  158. Swait, J.D., Louviere, J.: The role of the scale parameter in the estimation and comparison of multinomial logit models. J. Mark. Res. 30, 305–314 (1993)

    Google Scholar 

  159. Swords, A., Goldman, L., Feldman, W., Ehrlich, T., Bird Jr., W.: Analytical framework for prioritizing bicycle and pedestrian investments: new Jersey’s statewide master plan update, phase 2. Transp. Res. Record J. Transp. Res. Board 1878, 27–35 (2004)

    Google Scholar 

  160. Tal, G., Handy, S.: Measuring nonmotorized accessibility and connectivity in a robust pedestrian network. Transp. Res. Rec. J. Transp. Res. Board (2299), 48–56 (2012)

    Google Scholar 

  161. Talavera-Garcia, R., Soria-Lara, J.A.: Q-PLOS, developing an alternative walking index. A method based on urban design quality. Cities 45, 7–17 (2015)

    Google Scholar 

  162. Taylor, S.E.: Asymmetrical effects of positive and negative events: the mobilization-minimization hypothesis. Psychologist 38, 1161–1173 (1991)

    Google Scholar 

  163. Tian, G., Ewing, E.: A walk trip generation model for Portland, OR. Transp. Res. Part D. 1361–9209 (2017). http://dx.doi.org/10.1016/j.trd.2017.03.017

  164. Tribby, C.P., Miller, H.J., Brown, B.B., Werner, C.M., Smith, K.R.: Assessing built environment walkability using activity-space summary measures. J. Transp. Land Use 9(1), 187–207 (2016)

    Google Scholar 

  165. Tversky, A., Kahneman, D.: Advances in prospect theory: cumulative representation of uncertainty. J. Risk Uncertain. 5, 297–323 (1992)

    Google Scholar 

  166. U.S. Environmental Protection Agency: About Smart Growth. U.S. Environmental Protection Agency. http://www.epa.gov/smartgrowth/about_sg.htm (2008). Accessed 12 April 2017

  167. Vale, D.S., Saraiva, M., Pereira, M.: Active accessibility: a review of operational measures of walking and cycling accessibility. J. Transp. Land Use 9(1), 209–235 (2016)

    Google Scholar 

  168. Van Dyck, D., Cardon, G., Deforche, B., Sallis, J.F., Owen, N., De Bourdeaudhuij, I.: Neighborhood SES and walkability are related to physical activity behavior in Belgian adults. Prev. Med. 50(suppl 1), S74–S79 (2010)

    Google Scholar 

  169. Vargo, J., Stone, B., Glanz, K.: Google walkability: a new tool for local planning and public health research? J. Phys. Act. Health 9(5), 689–697 (2012)

    Google Scholar 

  170. Wang, Y., Chau, C.K.A., Ng, W.Y., Leung, T.M.: A review on the effects of physical built environment attributes on enhancing walking and cycling activity levels within residential neighborhoods. Cities 50(1), 1–15 (2016). https://doi.org/10.1016/j.cities.2015.08.004

    Article  Google Scholar 

  171. Weinstein Agrawal, A., Schlossberg, M., Irvin, K.: How far, by which route and why? A spatial analysis of pedestrian preference. J. Urban Des. (2008). https://doi.org/10.1080/13574800701804074

    Article  Google Scholar 

  172. Wells, N.M., Yang, Y.: Neighborhood design and walking: aquasi-experimental longitudinal study. Am. J. Prev. Med. 34(4), 313–319 (2008). https://doi.org/10.1016/j.amepre.2008.01.019

    Article  Google Scholar 

  173. Wey, W.M., Chiu, Y.H.: Assessing the walkability of pedestrian environment under the transit-oriented development. Habitat Int. 38, 106–118 (2013). https://doi.org/10.1016/j.habitatint.2012.05.004

    Article  Google Scholar 

  174. Woldeamanuel, M., Kent, A.: Measuring walk access to transit in terms of sidewalk availability, quality, and connectivity. J. Urban Plan. Dev. 142(2), 04015019 (2015)

    Google Scholar 

  175. Yin, L.: Assessing walkability in the city of buffalo: application of agent-based simulation. J. Urban Plan. Dev. 139(3), 166–175 (2013). https://doi.org/10.1061/(asce)up.1943-5444.0000147

    Article  Google Scholar 

  176. Zegras, C.: The built environment and motor vehicle ownership and use: evidence from Santiago de Chile. Urban Stud. 47(8), 1793–1817 (2010)

    Google Scholar 

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Acknowledgements

The authors thank the CNPQ for the financial support provided from project 407630/2016-3. Luis Rizzi acknowledges financial support from Institute in Complex Engineering Systems (CONICYT: FBO816) and the Centre for Sustainable Urban Development, (CONICYT/FONDAP/15110020). The authors are indebted to the three referees for their comments that improved the substance and readability of the paper.

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AML: Literature Search, Experiment Design, Modelling, Policy Simulation, Manuscript Writing; JA: Experiment Design, Modelling, Policy Simulation, Manuscript Writing; LIR: Content planning, Modelling and Manuscript Writing; OS Manuscript Writing, Review and Editing; HC: Experiment Design, Content and data planning, Manuscript Writing and Editing.

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Correspondence to Ana Margarita Larranaga.

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Larranaga, A.M., Arellana, J., Rizzi, L.I. et al. Using best–worst scaling to identify barriers to walkability: a study of Porto Alegre, Brazil. Transportation 46, 2347–2379 (2019). https://doi.org/10.1007/s11116-018-9944-x

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Keywords

  • Best–worst scaling
  • Discrete choice modelling
  • Walkability
  • Built environment barriers