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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]

Street attributes are thought to play an important role in influencing pedestrian route choices. Faced with alternatives, pedestrians have been observed to choose faster, safer, more comfortable, more interesting, or more beautiful routes. Literature on pedestrian route choice has provided methods for assessing the likelihood of such options using discrete choice models. However, route choice estimation, which is data intensive and computationally challenging, remains infrequently deployed in planning mobility analysis practice. Even when coefficients from previous studies are available, operationalizing them in foot-traffic predictions has been rare due to uncertainty involved in the transferability of behavioral effects from one context to another, as well as computational challenges of predicting route choice with custom attributes. This paper explores a simpler method of route choice prediction, implemented in the Urban Network Analysis toolbox, which assigns probabilities to available route options based on distance alone. We compare the accuracy of distance-weighted approaches to the more detailed utility-weighted approach using a large dataset of observed GPS pedestrian traces that include numerous trips between same intersections pairs in downtown San Francisco as a benchmark. Even though a utility-weighted model matches observed pedestrian flows most accurately, a distance-weighted model is only marginally inferior, on average. However, shortest-distance and highest-utility route predictions are both significantly inferior to the utility-weighted and distance-weighted sample-enumeration methods. Our findings suggest that simplified assumptions can be used to predict pedestrian flow in practice with existing software, opening pedestrian flow predictions to a wider range of planning and transportation applications. [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]