Bluetooth technology applications have improved travel time data collection efforts and allowed for collection of large data sets at a low cost per data unit. Mean travel times between pairs of points are available, but the primary value of this technique is the availability of the entire distribution of travel times throughout multiple days and time periods, allowing for a greater understanding of travel time variations and reliability. The use of these data for transportation planning, engineering and operations continues to expand. Previous applications of similar data sources have included travel demand and simulation model validation, work zone traffic patterns, transit ridership and reliability, pedestrian movement patterns, and before-after studies of transportation improvements. This thesis investigates the collection and analysis of Bluetooth-enabled travel time data along a multimodal arterial corridor in San Luis Obispo, California. Five BlueMAC devices collected multimodal travel time data in January and February 2016 along Los Osos Valley Road. These datasets were used to identify and process known sources of error such as occasions where vehicles using the roadway turn off and make an intermediate stop and multiple reads from the same vehicle; quantify travel time performance and reliability along arterial streets; and compare transit, bicycle, and pedestrian facility performance. Additionally, a travel time model was estimated based on segment characteristics and Bluetooth data to estimate average speeds and travel time distributions.