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The acceleration of urban imagery data analysis, driven by computer vision (CV), has created noteworthy opportunities for urban studies and planning. Data on street environments with high granularity derived from geo-tagged street views allow urban researchers to obtain geospatial data on greenery, pavement materials, and dimensions, building facades, urban furniture, lighting, vehicle presence, etc. However, how such attributes have been classified and used to address urban studies, planning, or mobility questions remains relatively poorly understood among non-technical researchers. Targeting urban planning and design researchers who do not have a background in CV, this paper reviews planning-relevant attributes that CV approaches of urban streetscapes have delivered to date and examines some of their research applications. We present a systematic analysis of 146 papers scrutinizing 104 street attributes in four groups. By exploring a subcollection of 24 papers, we discuss the effectiveness of those attributes being incorporated into current quantitative urban studies. This study's primary contribution lies in providing a comprehensive summary of CV-driven street attributes, their applications, and the algorithms used, serving as a valuable resource for future urban research. Additionally, we identify key challenges in this field, such as unclear definitions of attributes, a disproportionate emphasis on selecting models and features, and the absence of standardized measurement and definition methods. Furthermore, we offer recommendations for future research directions in this area. • We review 104 street attributes collected with computer vision in urban studies • We summarize 23 chosen attributes' correlations with known urban design measures • We provide a table of CV-driven street attributes with model details, references, and impacts • We highlight challenges in attribute definitions, research focus, and impact clarity • We provide recommendations for standardizing data and future research [ABSTRACT FROM AUTHOR]

This study adds to the nascent but growing literature on the use of big data for pedestrian route choice analysis. We explore behavioral preferences for various route attributes in Boston, MA using a large dataset of GPS trajectories (n = 11,165) sourced from a third-party smartphone app. Although the data are anonymized and limit our exploration of user heterogeneity, the sample size and area coverage are both much larger than seen in most previous studies. We estimate route choice preferences using a path size logit model, and operationalize the coefficients for policy-making through 'willingness-to-walk' measures. The value of these measures is demonstrated through an application of computing pedestrian accessibility to transit stations. Additionally, we compare our findings to a previous study in San Francisco, CA using similar data and methods, and previous literature to explore similarities and differences in pedestrian route choice behavior across major metropolitan areas more generally. While our findings can inform walkability policy and practice on several counts, we recommend future efforts to focus on supplementing this study by surveying hard-to-reach populations for more equitable policy-making. [ABSTRACT FROM AUTHOR]

As municipalities are setting ambitious targets to increase non-motorized transportation mode shares, analytic frameworks for examining how such targets will be met become increasingly important. In this article, we update the Urban Network Analysis pedestrian modeling framework designed to link land use and urban development changes with pedestrian mobility outcomes by introducing new methods for capturing the effects of street properties on pedestrian travel demand and adjusting pedestrian trip generation rates with k-nearest accessibility scores based on destination availability in each context. This framework was used in conjunction with a participatory design process in Beirut, Lebanon to estimate pedestrian mobility impacts of three urban design scenarios. We illustrate how each scenario affects pedestrian trip generation across various trip types and trip distribution on individual street segments. Our approach demonstrates how urban design interventions–both land use changes and street quality improvements–can influence pedestrian travel demand. Estimates of these changes can provide planners and policymakers with valuable benefit-cost analyses of public space improvements, and a framework for understanding how site-specific planning and development decisions can impact progress towards (or away from) non-motorized mobility goals. • We propose a pedestrian impact assessment framework to estimate how street or building changes affect pedestrian mobility. • We introduce "perceived" route lengths to account for street attribute effects on pedestrian travel demand. • We introduce the concept of of k-nearest destination accessibility, which enables elasticities in pedestrian trip generation. • The framework is used to estimate urban design impacts on trip distribution, and for the first time, also trip generation. • Using Beirut, Lebanon as example, the framework can guide development options to decarbonize urban mobility. [ABSTRACT FROM AUTHOR]

Urban planners and transportation policy makers around the world are proposing initiatives for greener mobility, particularly by promoting higher urban development densities, active transport modes, and non-auto access to destinations. This Special Issue engages critically with the sustainable mobility and 15-Minute City concepts by outlining an Equitable Sustainable Mobility Model that integrates non-auto accessibility improvements with key daily activity destinations to establish a wider discussion on urban structure, segregation, equity and mobility. The papers collected in this Special Issue reveal that sustainable mobility solutions are only partial when detached from the underlying mechanisms of residential sorting and spatial patterns of daily activity spaces. An equitable shift towards greener mobility needs to (1) address rising levels of residential segregation by promoting neighbourhood-level mixed-income housing, (2) radically shift urban space from automobility to different greener forms of mobility, (3) address not only neighbourhood specific, but also metropolitan-level access challenges to key activity places, (4) focus on integrating broadly accessible and affordable travel modes, particularly active travel (walking and cycling) and public transit, and (5) develop e-mobility solutions that are accessible to diverse user needs and offer flexible inter-neighbourhood coverage. [ABSTRACT FROM AUTHOR]

While cities around the world are increasingly promoting streets and public spaces that prioritize pedestrians over vehicles, significant data gaps have made pedestrian mapping, analysis, and modeling challenging to carry out. Most cities, even in industrialized economies, still lack information about the location and connectivity of their sidewalks, making it difficult to implement research on pedestrian infrastructure and holding the technology industry back from developing accurate, location-based Apps for pedestrians, wheelchair users, street vendors, and other sidewalk users. To address this gap, we have designed and implemented an end-to-end open-source tool— T ile 2N et —for extracting sidewalk, crosswalk, and footpath polygons from orthorectified aerial imagery using semantic segmentation. The segmentation model, trained on aerial imagery from Cambridge, MA, Washington DC, and New York City, offers the first open-source scene classification model for pedestrian infrastructure from sub-meter resolution aerial tiles, which can be used to generate planimetric sidewalk data in North American cities. T ile 2N et also generates pedestrian networks from the resulting polygons, which can be used to prepare datasets for pedestrian routing applications. The work offers a low-cost and scalable data collection methodology for systematically generating sidewalk network datasets, where orthorectified aerial imagery is available, contributing to over-due efforts to equalize data opportunities for pedestrians, particularly in cities that lack the resources necessary to collect such data using more conventional methods. • Introduces Tile2Net, an end-to-end open-source framework for creating georeferenced pedestrian networks from aerial imagery. • Implements the framework on Boston, Cambridge, New York City and Washington with a relatively high degree of accuracy. • Demonstrates a novel technique for creating semantic segmentation annotation masks from available planimetric GIS data. • The tool, open-source input data links and model weights and are made publicly available. [ABSTRACT FROM AUTHOR]

We examine differences in mobility outcomes between residents of highest and lowest socio-economic index (SEI) at the Census block group (CBG) level in nine major US cities prior to and during the COVID-19 pandemic. While low-SEI groups generally traveled shorter distances but visited more city-wide CBGs before the pandemic, high-SEI residents universally reduced their mobility to a greater extent during the pandemic. Although high-SEI residents were making more trips to parks and health-care providers, and fewer subsistence trips to retail stores already before the pandemic, COVID-19 significantly widened these differences thereby exacerbating "mobility gaps" between low-SEI and high-SEI groups. We further examine how such "mobility gaps" can be mitigated by spatial advantages of home locations, controlling for political inclination. We find that living in better transit-served or more walkable neighborhoods generally benefited high-SEI residents more than low-SEI residents, with some variation across cities. This suggests that built environments not only impact mobility outcomes during "normal" times, but also influence how different socio-economic groups are able to adapt during times of crisis. • We analyze pre- and during COVID-19 mobility behavior by socio-economic status based on over 139 million individual trips in nine U.S. cities. • High-SEI residents universally reduced their mobility to a greater extent during the pandemic. • We find a broad shift to automobile travel in transit-oriented areas during the pandemic. • Neighborhoods characteristics such as walkability and transit accessibility impact travel outcomes beyond income or socio-economic status. • Unaffordability of neighborhood amenities in walkable urban districts could push low SEI residents to travel further to more affordable destinations. [ABSTRACT FROM AUTHOR]

Among a number of variables shown to affect pedestrian route choice, path length and turns have stood out as the most consequential. Turns have been considered the superior variable by some architectural scholars of urban street networks, while transportation planners and geographers believe distance to be paramount. The longstanding debate between these two approaches has been reinvigorated with the emergence of big data and advanced computational methods. In this paper, we provide much-needed clarity to this debate by demonstrating how the relative effect of turns depends on the spatial properties of street networks. We postulate that certain properties of street networks make it possible to reduce the number of turns without substantially increasing route distance, and suggest that data from such environments are likely to show a large effect of turns on route choice. Conversely, networks where a reduction of in turns is necessarily accompanied by an increase in distance are expected to reduce the effect of turns. We test these hypotheses by examining the effects of both distance and turns on pedestrian route choice using a path size logit model calibrated on over 10,000 anonymized GPS traces of pedestrians each in San Francisco, CA and Boston, MA. We find that the effect of distance is consistently larger in magnitude, while the effect of turns depends on network geometry. Only in specific street networks can turns alone explain route choice behavior as well as distance. Our findings suggest that turns, as well as other environmental qualities of a route, should be considered in addition to, not in lieu of, distance. [ABSTRACT FROM AUTHOR]

Advances in computational urbanism have stimulated the rise of generative and parametric approaches to urban design. Yet, most generative and parametric approaches focus on physical characteristics, such as a neighborhoods walkability, energy efficiency, and urban form. Here, we study the colocation patterns of more than one million amenities in 47 U.S. cities to model the amenity mix of neighborhoods, and to identify the amenities that are over- or under-supplied in a neighborhood. We build this model by combining a clustering algorithm, designed to identify amenity-dense neighborhoods, and a network, connecting amenities that are likely to collocate. Our findings extend generative and parametric urban design approaches to the amenity mix of neighborhoods, by leveraging the idea of relatedness from the economic geography literature, to evaluate and optimize a neighborhood's amenity mix. [ABSTRACT FROM AUTHOR]