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This study provides the first comprehensive analysis of five years of detector data for two permanent bicycle count stations (representing four locations) on multi-use paths in Boulder, CO. First, temporal patterns of daily bicycle counts were explored. A strong linear correlation between high temperature and daily counts was noted with a slight decrease in counts at temperatures greater than 90
F. Counts also decreased on days with rain or snow, although this effect was not linear. The results of the temporal pattern and weather correlation analyses were used to derive the factors in the regression model that was developed to estimate daily volumes for those days missing counts. The daily counts and estimates were combined to generate annual volumes for each year in the study time period. Finally, annual estimates were adjusted based on model residuals and were used to develop trendlines for forecasting volumes at these locations in the year 2005. Based on these estimates, although the volumes fluctuate, three out of the four locations have exhibited a slightly decreasing trend over the past five years.

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Amy Lewin, PE, is a Senior Transportation Engineer with URS Corporation in Denver, CO. She focuses on multimodal travel demand forecasting and transportation data analysis with a particular interest in transit and bicycle and pedestrian planning. Amy is an Eno Transportation Foundation Fellow and was WTS Member of the Year in 2007.

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