Bicycling as an alternate mode of transportation has been on the rise. It is environmentally friendly in nature and the associated health benefits have made it a popular choice for many types of trips. The purpose of this research is to increase understanding of the impacts of implementing bicycle corridors (as part of the Utah Department of Transportation's (UDOT) Inclusion of Active Transportation policy) on bicycle rate as a function of roadway characteristics. The results of this research will be used in determining when and where bicycle corridors will enhance the transportation system and an estimate of the overall impact of bicycle corridors on travel demand in Utah. Data collection was fundamental in this research project in determining the impacts of bicycle corridors on travel demands in the state of Utah. With limited amount of commuting bicycle data available throughout the state, it was necessary to gather bicycle volume data on corridors with and without bicycle infrastructure. In order to accomplish this data collection effort, two primary methods were used to collect bicycle volume data. The first method was to use automatic bicycle counters on roadways that had bicycle infrastructure. The second method was to gather bicycle volume data through manual counts on roads with and without bicycle infrastructure. After the bicycle volume data were collected the data were analyzed to identify trends. The first step in the analysis was to convert the bicycle volumes into rates to provide a more uniform comparison. Several analyses were run including an analysis of bicycle rate compared to Annual Average Daily Traffic (AADT), bicycle rate compared to posted speed limit, bicycle rate compared to number of vehicle lanes, and bicycle rate compared to roadway classification. A comparison of sites with bicycle infrastructure to sites without bicycle infrastructure (non-bicycle infrastructure) was also conducted to identify relationships. Comparison of bicycle rates to AADT resulted in no correlation or statistical relationship in the data but the data do suggest trends. Statistically significant results did occur when comparing bicycle rates to posted speed limits. No statistically significant relationships occurred when comparing bicycle rates to the number of lanes or roadway classification. It was determined that roadways with bicycle infrastructure tend to yield higher bicycle rates than roadways that do not have bicycle infrastructure. Lastly, using shared use path data it is determined that bicycle rates on shared use paths have increased between 1.7 to 7.5 percent from 2013 to 2014 and it is assumed that a similar trend would exist on bicycle infrastructure in the communities.