Longitudinal Analysis of Bicycle Count Variability: Results and Modeling Implications

Abstract
This research examines composition, weather, and time-of-year count variability for a longitudinal bicycle count program. The results indicate greater variability in the PM peak period than in the AM peak period, and this variability is at least partially attributable to the presence of nonutility cyclists. Second, all locations display the same functional form for count volume by both temperature and amount of precipitation, and it appears that temperature may be a better predictor of volume than the amount of precipitation for these data. Third, the results of a Poisson model statistically confirm many of the factors thought to influence cyclists, and identify that a single count volume may be biased by as much as ±15% depending on the time of year in which the count was undertaken. Finally, a new bicycle functional classification system based on PM peak-period composition is proposed.