Individuals who walk and cycle experience a variety of health and economic benefits while simultaneously benefiting their local environments and communities. It is essential to correctly obtain pedestrian and bicyclist counts for better design and planning of active transportation-related facilities. In recent years, crowdsourcing has seen a rise in popularity due to the multiple advantages relative to traditional methods. Nevertheless, crowdsourced data have been applied in fewer studies, and their reliability and performance relative to other conventional methods are rarely documented. To this end, this research examines the consistency between crowdsourced and traditionally collected count data. Additionally, the research aims to develop the adjustment factor between the crowdsourced and permanent counter data and to estimate the annual average daily traffic (AADT) data based on hourly volume and other predictor variables such as time, day, weather, land use, and facility type. With some caveats, the results demonstrate that the Street Light crowdsourcing count data for pedestrians and bicyclists appear to be a promising alternative to the permanent counters.
Digital Object Identifier
Mineta Transportation Institute URL
Active Transportation, Pedestrian, Bicyclist, Count, Crowdsourcing
Infrastructure | Transportation | Urban Studies and Planning
Wen Cheng, Yongping Zhang, and Edward Clay. "Comprehensive Performance Assessment of Passive Crowdsourcing for Counting Pedestrians and Bikes" Mineta Transportation Institute Publications (2022). https://doi.org/10.31979/mti.2022.2025