Schmiedeskamp, Peter; Zhao, Weiran. (2016). Estimating Daily Bicycle Counts in Seattle, Washington, from Seasonal and Weather Factors. Transportation Research Record, 2593, 94 – 102.
Abstract
This paper examines the relationship between several seasonal and weather factors and bicycle ridership from 2 years of automated bicycle counts at a location in Seattle, Washington. The authors fitted a negative binomial model and then estimated quantities of interest using counterfactual simulation. The findings confirm the significance of season (+), temperature (+), precipitation (), as well as holidays (-), day of the week (+ for Monday through Saturday, relative to Sunday), and an overall trend (+). This paper improves on prior work by demonstrating the use of the negative binomial instead of a Poisson model, which is appropriate given the potential for overdispersion, as observed in these data. In addition to validating the significance of factors identified from the literature, this paper contributes methodologically through its intuitive visualization of effect sizes to nonstatistical audiences. The authors believe that the combination of model type and counterfactual simulation and visualization reflects a reasonable compromise between model complexity and interpretability. Results such as these can aid policy makers and planners in understanding bicycle travel demand elasticities and in guiding interventions aimed at increasing rates of bicycling. The methods presented are fully reproducible and invite adaptation to other locations.