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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]

City governments and planners alike commonly seek to increase pedestrian activity on city streets as part of broader sustainability, community building, and economic development strategies. Though walkability has received ample attention in planning literature, most planners still lack practical methods for predicting how development proposals could affect pedestrian activity on specific streets or public spaces at different times of the day. Cities typically require traffic impact assessments (TIAs) but not pedestrian impact assessments. In this study I present a methodology for estimating pedestrian trip generation and distribution between detailed origins and destinations in both existing and proposed built environments. Using the betweenness index from network analysis, I introduce a number of methodological improvements that allow the index to model pedestrian trips with parameters and constraints to account for pedestrian behavior in different settings. I demonstrate its application in the Kendall Square area of Cambridge (MA), where estimated foot traffic is compared during lunch and evening peak periods with observed pedestrian counts. The proposed approach can be particularly useful for TIAs, neighborhood plans, and large-scale development projects, where pedestrian flow estimates can be used to guide pedestrian infrastructure and safety improvements and public space investments or for locating pedestrian priority streets during the COVID-19 pandemic. [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]