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.

As concerns about climate change increase, many people are calling for reductions in the use of fossil fuels and encouraging a shift to more sustainable and less polluting transportation modes. Cities and urban areas are more concerned because their population currently comprises over half of the world's population. Sustainable transportation modes such as cycling, walking, and use of public transit and electric vehicles can benefit the environment in many ways, including a reduction in toxic greenhouse gas (GHG) emissions and noise levels. In order to enhance the trend of using sustainable modes of transportation, tools, measures, and planning techniques similar to those used for vehicular transportation need to be developed. In this dissertation, we consider four problems in the context of different sustainable modes of transportation, namely, cycling, rail, public transit, and ridesharing. We develop different models to predict bike travel times for use in bike share systems (BSSs) using random forest (RF), least square boosting (LSBoost), and artificial neural network (ANN) techniques. We also use cycling Global Positioning System (GPS) data collected from 10 people (3 females and 7 males) to study cyclists' acceleration/deceleration behavior. Moreover, we develop a continuous rail transit simulator (RailSIM) intended for multi-modal energy-efficient routing applications. Finally, we propose a dynamic trip planning system that integrates ridesharing and public transit. The work done in this dissertation can help encouraging more people to move to more sustainable modes of transportation. / Doctor of Philosophy / As concerns about climate change increase, many people are calling for reductions in the use of fossil fuels and encouraging a shift to more sustainable and less polluting transportation modes. Cities and urban areas are more concerned because their population currently comprises over half of the world's population. Sustainable transportation modes such as cycling, walking, and use of public transit and electric vehicles can benefit the environment in many ways, including a reduction of toxic greenhouse gas (GHG) emissions and noise levels. In order to enhance the trend of using sustainable modes of transportation, tools, measures, and planning techniques similar to those used for vehicular transportation need to be developed. In this dissertation, we consider four problems in the context of different sustainable modes of transportation, namely, cycling, rail, public transit, and ridesharing. We develop different models to predict bike travel times in bike share systems (BSSs) using machine learning techniques. We also use cycling Global Positioning System (GPS) data collected from 10 people (3 females and 7 males) to study cyclists' acceleration/deceleration behavior. Moreover, we develop a continuous rail transit simulator (RailSIM) intended for multi-modal energy-efficient routing applications. Finally, we propose a dynamic trip planning system that integrates ridesharing and public transit. The work done in this dissertation can help encouraging more people to move to more sustainable modes of transportation.