A street-point method to measure the spatiotemporal relationship between walkability and pedestrian flow

Abstract

Walkability indicators are a pivotal method to evaluate the role of the built environment in peoples' decisions regarding active mobility, supporting the application of public measures that contribute to more sustainable and resilient regions. Currently, data used to evaluate associations between walkability indicators and travel behavior is obtained via traditional methods of data collection, like questionnaires, that are costly and hard to scale in large urban environments. Moreover, the spatial resolution of most indicators may not be sufficient to support granular local public interventions. To face these issues, we propose a novel walkability indicator that provides a score of walkability for every one-meter street point, based on street conditions and accessibility to points of interest calculated with a Cumulative-Gaussian impedance function. Resorting to Linear and Geospatial Weighted Regressions, we evaluate the associations between walkability features and pedestrian flow data retrieved from mobile phone communication signals for a week in March 2022. The relationship between walkability features and pedestrian flow is stronger during workdays, in which accessibility to education, food amenities and government services are the most important predictors. On the weekend, the features with more explanatory power are accessibility to crosswalks and leisure amenities. Accessibility to public transport, sidewalk width and slope seem to impact pedestrian decisions independently of the day type, although the impact is stronger on weekends. This study provides policy makers and urban planners with a practical tool to effectively support the evaluation of current street conditions and access areas that are underserved, as well as plan and gauge new local interventions, while objectively understanding their impacts on pedestrian mobility.