A systematic review of crowdsourced pedestrian data

Abstract

Cities are increasing investments in walkable environments due to the health, environmental, and social benefits of walking. However, accurately measuring pedestrian patterns remains challenging due to data limitations. Crowdsourced data presents an opportunity to address data limitations but there are also challenges. This paper aims to take a systematic look into crowdsourced data that are being used to fill gaps and advance pedestrian research with three primary objectives: (1) to identify key gaps and opportunities in the current application of crowdsourced data in pedestrian-related research, (2) to critically evaluate the methods used to process and integrate crowdsourced data for pedestrian research, and (3) to provide key recommendations to guide future research and planning efforts through the effective use of crowdsourced data in pedestrian studies. Conducting a systematic literature review, we screened an initial set of 653 papers to identify 48 peer-reviewed studies published between 2011 and 2024 for full review. We identify eight thematic uses of crowdsourced data in pedestrian research and planning: travel patterns, infrastructure detection, mode detection, navigation, policy, route/infrastructure choice, safety, and walkability/accessibility. Additionally, we highlight four promising future research opportunities: understanding disruptions that shape pedestrian mobility, incorporating equity considerations into research and planning, integrating pedestrian perspectives into crowdsourced imagery, and refining methodologies for analyzing crowdsourced data. We provide a guide for both researchers and policy makers in advancing pedestrian research through the integration of crowdsourced and traditional data sources.