Application of multi-criteria decision analysis methods for assessing walkability: A case study in Porto Alegre, Brazil

Abstract The concept of walkability refers to the extent to which a neighbourhood is walking-friendly. Several walkability indexes have been developed to quantify and evaluate the pedestrian environment. These indexes differ in terms of type of data, methods and goals. The indexes variables may present either uniform or distinct weights, defined by arbitrary, empirical or other diverse weighting methods. This paper pursues the determination of a weighted walkability index, constructed on the basis of the relative importance of their attributes. Weights were determined by the application of the Fuzzy Analytic Hierarchy Process (FAHP), a robust multi-criteria method which considers the experts’ uncertainty in decision making. Moreover, FAHP weights were compared with the attribute weights obtained from other simpler methods, and a chi-square test for homogeneity was computed to compare the obtained values. The three most important walkability attributes were: Public Security, Traffic Safety and Pavement Quality, similar results to the ones found in the literature. The application to a case study in the city of Porto Alegre, Brazil, allowed categorizing the studied neighbourhoods and to analyse the effect of changes on attributes in walkability.

[1]  A. M. Larrañaga,et al.  THE RELATIONSHIP BETWEEN BUILT ENVIRONMENT AND WALKING FOR DIFFERENT TRIP PURPOSES IN PORTO ALEGRE, BRAZIL , 2014 .

[2]  Valentinas Podvezko,et al.  MCDM Assessment of a Healthy and Safe Built Environment According to Sustainable Development Principles: A Practical Neighborhood Approach in Vilnius , 2017 .

[3]  Brian Stone,et al.  Google walkability: a new tool for local planning and public health research? , 2012, Journal of physical activity & health.

[4]  Wann-Ming Wey,et al.  Assessing the walkability of pedestrian environment under the transit-oriented development , 2013 .

[5]  Reid Ewing,et al.  Travel and the Built Environment , 2010 .

[6]  Yue Liu,et al.  Evaluating Transit Operator Efficiency: An Enhanced DEA Model with Constrained Fuzzy-AHP Cones , 2016 .

[7]  Susan L Handy,et al.  Measuring the Unmeasurable: Urban Design Qualities Related to Walkability , 2009 .

[8]  A. Awasthi,et al.  AHP-Based Approach for Location Planning of Pedestrian Zones: Application in Montreal, Canada , 2013 .

[9]  Vikas Mehta,et al.  Walkable streets: pedestrian behavior, perceptions and attitudes , 2008 .

[10]  Erick Guerra,et al.  The Built Environment and Car Use in Mexico City , 2014 .

[11]  Dan Burden,et al.  Building Communities with Transportation , 2001 .

[12]  B. Smoller Systems review. , 2010, Maryland medicine : MM : a publication of MEDCHI, the Maryland State Medical Society.

[13]  Marta Herva,et al.  Review of combined approaches and multi-criteria analysis for corporate environmental evaluation , 2013 .

[14]  Carol M. Werner,et al.  Geographic regions for assessing built environmental correlates with walking trips: A comparison using different metrics and model designs , 2017, Health & place.

[15]  Sugie Lee,et al.  Residential built environment and walking activity: Empirical evidence of Jane Jacobs’ urban vitality , 2015 .

[16]  R. Cervero,et al.  TRAVEL DEMAND AND THE 3DS: DENSITY, DIVERSITY, AND DESIGN , 1997 .

[17]  B. Saelens,et al.  Built environment correlates of walking: a review. , 2008, Medicine and science in sports and exercise.

[18]  Carol M. Werner,et al.  Assessing Built Environment Walkability using Activity-Space Summary Measures. , 2016, Journal of transport and land use.

[19]  C. F. Kossack,et al.  Rank Correlation Methods , 1949 .

[20]  Patrick A. Singleton,et al.  Safety and Security in Discretionary Travel Decision Making , 2014 .

[21]  Katarzyna Solecka,et al.  Application of AHP method for multi-criteria evaluation of variants of the integration of urban public transport , 2014 .

[22]  Reid Ewing,et al.  A walk trip generation model for Portland, OR , 2017 .

[23]  Portland Portland : Pedestrian master plan , 1998 .

[24]  Alejandro Ruiz-Padillo,et al.  Social choice functions: A tool for ranking variables involved in action plans against road noise. , 2016, Journal of environmental management.

[25]  Yiik Diew Wong,et al.  Influence of infrastructural compatibility factors on walking and cycling route choices , 2013 .

[26]  Sungjin Park,et al.  Meso- or micro-scale? Environmental factors influencing pedestrian satisfaction , 2014 .

[27]  J. Sallis,et al.  Environmental correlates of walking and cycling: Findings from the transportation, urban design, and planning literatures , 2003, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.

[28]  Åse Svensson,et al.  Satisfaction or compensation? The interaction between walking preferences and neighbourhood design , 2017 .

[29]  Bojan Srdjevic,et al.  Linking analytic hierarchy process and social choice methods to support group decision-making in water management , 2007, Decis. Support Syst..

[30]  Hani S. Mahmassani,et al.  Impact of Crime Statistics on Travel Mode Choice , 2015 .

[31]  C. Kahraman,et al.  Multi‐criteria supplier selection using fuzzy AHP , 2003 .

[32]  Alejandro Tudela,et al.  Comparing the output of cost benefit and multi-criteria analysis: An application to urban transport investments , 2006 .

[33]  C. Zegras The Built Environment and Motor Vehicle Ownership and Use: Evidence from Santiago de Chile , 2010 .

[34]  B. Giles-Corti,et al.  Neighbourhood design and fear of crime: a social-ecological examination of the correlates of residents' fear in new suburban housing developments. , 2010, Health & place.

[35]  R. Cervero,et al.  Influences of Built Environments on Walking and Cycling: Lessons from Bogotá , 2009 .

[36]  Maria Kamargianni,et al.  “Teenager's Travel Patterns for School and After-School Activities.” , 2012 .

[37]  Carlos Angel Iglesias,et al.  Evaluating social choice techniques into intelligent environments by agent based social simulation , 2014, Inf. Sci..

[38]  Iderlina Mateo-Babiano,et al.  Pedestrian's needs matter: Examining Manila's walking environment , 2016 .

[39]  Charles R. Shipan,et al.  A social choice approach to expert consensus panels. , 2004, Journal of health economics.

[40]  José Carlos Rodriguez Alcantud,et al.  A unifying model to measure consensus solutions in a society , 2013, Math. Comput. Model..

[41]  Kathryn M Neckerman,et al.  Neighborhood safety and green space as predictors of obesity among preschool children from low-income families in New York City. , 2013, Preventive medicine.

[42]  E. Guerra,et al.  Urban form, transit supply, and travel behavior in Latin America: Evidence from Mexico's 100 largest urban areas , 2018, Transport Policy.

[43]  Alejandro Ruiz-Padillo,et al.  Application of the fuzzy analytic hierarchy process in multi-criteria decision in noise action plans: Prioritizing road stretches , 2016, Environ. Model. Softw..

[44]  Sylvain Kubler,et al.  A state-of the-art survey & testbed of fuzzy AHP (FAHP) applications , 2016, Expert Syst. Appl..

[45]  J. M. Bevan,et al.  Rank Correlation Methods , 1949 .

[46]  F. Moura,et al.  Measuring walkability for distinct pedestrian groups with a participatory assessment method: A case study in Lisbon , 2017 .

[47]  Anna Bahyrycz Construction of systems of sets related to the plurality functions , 2012 .

[48]  Ioannis Kaparias,et al.  Analysing the perceptions of pedestrians and drivers to shared space , 2012 .

[49]  Ran Wei,et al.  Walkability, Land Use and Physical Activity , 2016 .

[50]  David S. Vale,et al.  Active accessibility: A review of operational measures of walking and cycling accessibility , 2016 .

[51]  T. Litman London Congestion Pricing – Implications for Other Cities , 2005 .

[52]  Anjali Awasthi,et al.  A fuzzy multicriteria approach for evaluating environmental performance of suppliers , 2010 .

[53]  Edmundas Kazimieras Zavadskas,et al.  Fuzzy multiple criteria decision-making techniques and applications - Two decades review from 1994 to 2014 , 2015, Expert Syst. Appl..

[54]  José Luis Míguez,et al.  The use of grey-based methods in multi-criteria decision analysis for the evaluation of sustainable energy systems: A review , 2015 .

[55]  Jorge Curiel-Esparza,et al.  Prioritization by consensus of enhancements for sustainable mobility in urban areas , 2016 .

[56]  Helena Beatriz Bettella Cybis,et al.  The influence of built environment and travel attitudes on walking: A case study of Porto Alegre, Brazil , 2016 .

[57]  A. Bauman,et al.  Residents' perceptions of walkability attributes in objectively different neighbourhoods: a pilot study. , 2005, Health & place.

[58]  Yen-Cheng Chiang,et al.  Using expert decision-making to establish indicators of urban friendliness for walking environments: a multidisciplinary assessment , 2016, International Journal of Health Geographics.

[59]  Holly Krambeck,et al.  The global walkability index , 2006 .

[60]  Jae Seung Lee,et al.  Perception-Based Walkability Index to Test Impact of Microlevel Walkability on Sustainable Mode Choice Decisions , 2014 .

[61]  Hui Sun,et al.  A social stakeholder support assessment of low-carbon transport policy based on multi-actor multi-criteria analysis: The case of Tianjin , 2015 .

[62]  Darren M. Scott,et al.  Examining the relationship between active travel, weather, and the built environment: a multilevel approach using a GPS-enhanced dataset , 2013, Transportation.

[63]  Stefano Tarantola,et al.  Handbook on Constructing Composite Indicators: Methodology and User Guide , 2005 .

[64]  Christine M. Hoehner,et al.  Measuring the built environment for physical activity: state of the science. , 2009, American journal of preventive medicine.

[65]  Reid Ewing,et al.  Measuring Urban Design: Metrics for Livable Places , 2013 .

[66]  Marco Petrelli,et al.  Walkability Indicators for Pedestrian-Friendly Design , 2014 .

[67]  Takemi Sugiyama,et al.  Do Relationships Between Environmental Attributes and Recreational Walking Vary According to Area-Level Socioeconomic Status? , 2015, Journal of Urban Health.

[68]  J. Sallis,et al.  Neighborhood Environment Walkability Scale: validity and development of a short form. , 2006, Medicine and science in sports and exercise.

[69]  A. Moudon,et al.  Effects of Site Design on Pedestrian Travel in Mixed-Use, Medium-Density Environments , 1997 .

[70]  Soledad Nogués,et al.  Multi-criteria impacts assessment for ranking highway projects in Northwest Spain , 2014 .

[71]  Lotfi A. Zadeh,et al.  Soft computing and fuzzy logic , 1994, IEEE Software.

[72]  T. Saaty,et al.  The Analytic Hierarchy Process , 1985 .

[73]  Cathal M. Brugha,et al.  Structure of multi-criteria decision-making , 2004, J. Oper. Res. Soc..

[74]  A Allan Walking As a Local Transport Modal Choice in Adelaide , 2001 .

[75]  Richa Singh,et al.  Factors Affecting Walkability of Neighborhoods , 2016 .

[76]  Rehan Sadiq,et al.  Multiple stakeholders in multi-criteria decision-making in the context of Municipal Solid Waste Management: A review. , 2015, Waste management.

[77]  Daniela Bubboloni,et al.  On the reversal bias of the Minimax social choice correspondence , 2016, Math. Soc. Sci..

[78]  Jennie Middleton,et al.  ‘Stepping in Time’: Walking, Time, and Space in the City , 2009 .

[79]  Henry C. W. Lau,et al.  A fuzzy multi-criteria decision support procedure for enhancing information delivery in extended enterprise networks , 2003 .

[80]  Miles Tight,et al.  A comparison of three methods for assessing the walkability of the pedestrian environment , 2011 .