The use of crowdsourced mobile data in estimating pedestrian and bicycle traffic: A systematic review

Abstract

To address the need for better non-motorized traffic data, policymakers and researchers are collaborating to develop new approaches and methods for estimating pedestrian and bicyclist traffic volumes. Crowdsourced mobile data, which has higher spatial and temporal coverage and lower collection costs than data collected through traditional approaches, may help improve pedestrian and bicyclist traffic estimation despite their limitations or biases. This systemic literature review documents how researchers have used crowdsourced mobile data to estimate pedestrian and bicyclist traffic volumes. We find that one source of commercial fitness application data (i.e., Strava) has been used much more frequently than other crowdsourced mobile data, and that most studies have used crowdsourced mobile data to estimate bicyclist volumes. Comparatively few studies have estimated pedestrian volumes. The most common approach to the use of crowdsourced counts is as independent variables in direct demand models. Variables constructed from crowdsourced mobile data not only have significant correlations with observed counts in statistical models but also have larger relative importance than other factors in machine learning models. Studies also show that including crowdsourced mobile data can significantly improve estimation performance. Future research directions include application of crowdsourced mobile data in more pedestrian traffic estimations, comparison of the performance of different crowdsourced mobile data, incorporation of multiple data sources, and expansion of the methods using crowdsourced mobile data for non-motorized traffic estimation.

Keywords

• Strava
• non-motorized traffic
• direct demand model
• traffic estimation