Urban Network Analysis: Tools for Modeling Pedestrian and Bicycle Trips in CitiesAndres Sevtsuk
Design of the built environment – the spatial arrangement of buildings, blocks, streets, public spaces and the socio-economic functions they house – produces a variety of influences on urban mobility patterns and mode choices. Sprawling developments, where destinations are far apart and routes between them wide and fast, incentivize motorized trips. High density, mixed-use environments, with diverse destinations connected through a network of quality sidewalks, incentivize walking, biking and face-to-face encounter. City form and land-use patterns influence whether, how often and along which paths people choose to walk.
A robust body of planning literature has emerged to articulate the qualities that make urban environments walkable and bicycle friendly. Walkability is typically associated with i) the availability of useful and diverse destinations within walking distances (e.g. retail, service and employment establishments, transit stations) , ii) safe routes that do not put pedestrians in physical or psychological danger (e.g. do not require walking next to heavy traffic, crossing wide intersections or lack barriers to separate walkways from danger zones); iii) physical and environmental comfort of the routes (e.g. step-free access, even pavements, sufficient width of sidewalks, shading from sun and rain), as well as iv) interesting routes – routes that are lined with businesses, stimulating architecture, green spaces or compelling vistas (Pushkarev and Zupan 1975; Gehl 1987; Speck 2013). However, despite a rich literature on qualities of built environments that bring people out on foot or by bike, practical methods for measuring, analyzing and modeling active mobility remain lacking in practice. Much of transportation literature on pedestrian mobility relies on rather crude proxy metrics to evaluate the walkability of a place – intersection density, block size, population or employment density and land-use mix at the census tract level are often used as predictors (Boaernet et al., 2011; Cervero and Duncan 2003; Ewing and Cervero, 2010; Hess et al., 1999; Targa and Clifton, 2005). While useful for characterizing walkability at the aggregate, whole neighborhood level, density metrics and neighborhood summary statistics do not capture the influence of built environments on mobility behavior at the individual trip scale, where decisions to undertake walks actually start.
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