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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]

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]

Does the distribution of Rome's population follow routine hourly, daily, or weekly patterns? And if it does, how do such patterns vary in different parts of the city? This paper reports on our investigation of the aggregate patterns of urban mobility in Rome, Italy for which we used novel data from a mobile phone operator. Unlike research that chartered urban mobility through individual travel surveys, our research determined the aggregate distribution of Rome's population over time by using the volume of call activity in mobile network cells as the unit of spatial analysis. In this paper, we first illustrate and confirm that there is significant regularity in urban mobility at different hours, days, and weeks. We then show how mobility between network cells differs at various times, and we account for the differences by using demographic, economic, and (built) environment indicators. [ABSTRACT FROM AUTHOR]