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The global climate-change crisis, along with public health and economic competitiveness challenges in cities around the world have underscored the need for analytic tools to examine the relationship between city design and sustainable mobility. Car-centered travel demand models and land-use-transportation interaction models have historically analyzed zone-to-zone trips along major roadways, largely omitting pedestrian and bicycle trips and creating a gap in the ability for planners and urban designers to systematically assess non-motorized outcomes of development interventions. We present the Urban Network Analysis tools to address this gap. UNA tools offer an accessibility-based framework for analyzing how built environments influence pedestrian travel in both existing and newly planned built environments. Developed as a free plugin for Rhinoceros 3D since 2015 and applied in several cities and research projects internationally, this paper describes the current Urban Network Analysis modeling framework and discusses the unique contributions the framework offers compared to existing pedestrian modeling approaches. Using Somerville, MA as case example, we demonstrate several commonly used functions for planners: examining pedestrian accessibility over networks; identifying critical walking routes to destinations; estimating foot-traffic on street segments; identifying frustration points for pedestrians; and evaluating how development changes may impact pedestrian activity in their vicinity. Such analyses can provide analytic evidence to pedestrian infrastructure planning and investment, and enable planners, designers, and policy makers to prioritize projects that increase sustainable mobility outcomes.

We introduce a version of the Huff retail expenditure model, where retail demand depends on households’ access to retail centers. Household-level survey data suggest that total retail visits in a system of retail centers depends on the relative location pattern of stores and customers. This dependence opens up an important question—could overall visits to retail centers be increased with a more efficient spatial configuration of centers in planned new towns? To answer this question, we implement the model as an Urban Network Analysis tool in Rhinoceros 3D, where facility patronage can be analyzed along spatial networks and apply it in the context of the Punggol New Town in Singapore. Using fixed household locations, we first test how estimated store visits are affected by the assumption of whether shoppers come from homes or visit shops en routeto local public transit stations. We then explore how adjusting both the locations and sizes of commercial centers can maximize overall visits, using automated simulations to test a large number of scenarios. The results show that location and size adjustments to already planned retail centers in a town can yield a 10% increase in estimated store visits. The methodology and tools developed for this analysis can be extended to other context for planning and right-sizing retail developments and other public facilities so as to maximize both user access and facilities usage.

AbstractThis paper investigates the relationship between street properties and cognitive maps. It is focused on the question of how human cognition of the built environment is affected by street properties. Building on the foundations of Kevin Lynch's studies of environmental perception (Lynch 1960) and recent configuration measurement techniques of the built environment, it addresses an important question that Lynch has left unresolved: Why do people have more complete recollections of some parts of the urban environment, and not others?This paper proposes an analytical measurement framework based on graph theory to compare the results of cognitive maps with objective spatial properties of the corresponding built environment. In order to test our hypothesis, street geometry is measured and defined based on graph theory in two selected areas with similar geometries in Kenmore, Boston and Kendall Sq., Cambridge, MA. Cognitive maps are then collected based on specifically designed map drawing surveys. Finally, the relationship between graph results and cognitive maps is examined in order to identify the ways that street properties affect human perception.

•Some of the key innovations presented in the paper include (a) gathering and mapping route attribute information by computationally processing a large set of Google Street View images, (b) using a novel technique for generating alternative paths for route choice estimation;•and (c) estimating how a range of specific route attributes affect pedestrian route choice from a large number of actual trajectories (i.e., revealed preference) that do not focus on a particular destination type (e.g. walking to transit), but include a wide diversity of destinations and geographies, thereby describing typical pedestrians’ preferences in San Francisco as a whole.•We also show how the findings can be operationalized to describe pedestrian accessibility to different destinations using ‘perceived distance’ as opposed to traversed distance.