Yang, MofengIn the past two decades, along with the technological advancement in mobile sensors and mobile networks, transportation big data, such as probe vehicle data and mobile device location data (MDLD), have been growing dramatically in terms of the spatiotemporal coverage of population and its mobility. These data sources have shown their great potential for large-scale and near real-time transportation applications to support travel behavior analysis, travel demand modeling, traffic operations and safety analyses. The objectives of this dissertation are to (1) comprehensively examine the state-of-the-practice applications and the state-of-the-art models developed based on emerging transportation big data, (2) identify key metrics, and (3) establish a series of big-data driven frameworks to enhance traffic operations and safety. Three main sections are included. The first section of this dissertation presents a literature review on models, tools, and metrics used for various levels of traffic analysis, and analyzes a survey distributed to transportation professionals to quantify the importance of these key metrics for improving traffic operations and safety. Based on the literature review and survey insights, two big-data driven frameworks are proposed accordingly to address both traffic operations and safety issues. In the second section of this dissertation, a big-data driven framework is developed which aims at improving the accuracy and reliability of emergency medical services (EMS) and trauma triage decisions for elderly persons at crash sites. The proposed framework integrates transportation big data sources from both the demand side (such as traffic volumes, and time-dependent vehicle speeds obtained from large-scale probe vehicles) and the supply side (i.e., transportation network features), as well as publicly available statewide crash data with health-related decisions such as EMS and hospital records. Decision tree models are adopted to simulate the decision-making process due to their wide applications, a proven capability in prediction, and interoperability. With records of over 55,000 elderly patients, results demonstrate that the proposed framework contributed to enhanced EMS decision and trauma triage accuracy for the elderly, and saving more lives from severe vehicle crashes. In the third section of this dissertation, a big-data driven framework is proposed for estimating a critical operational metric, namely vehicle volume, on an all-street network, and further estimating the pedestrian and bicyclist crashes at all intersections. This framework employs a series of cloud-based computational algorithms to extract multimodal trajectories and trip rosters from terabytes of MDLD. A scalable map matching and routing algorithm is then applied to snap and route vehicle trajectories to the roadway network. The observed vehicle counts on each roadway segment are weighted and calibrated against ground truth control totals, i.e., Annual Vehicle Miles of Travel (AVMT), and Annual Average Daily Traffic (AADT). The proposed framework is built on Amazon Web Service (AWS) which leverages cloud computing techniques to estimate vehicle volumes for all roadway segments in the state of Maryland using MDLD for the entire year 2019. The estimated vehicle volume is further integrated with statewide crash records to estimate the pedestrian and bicyclist crashes at all intersections with statistical models. Results indicate that the proposed framework can produce reliable vehicle volume estimates and estimated pedestrian and bicyclist crashes, while also demonstrating its transferability and generalization ability. In summary, this dissertation comprehensively examines the literature on transportation big data applications and proposes two big-data driven frameworks demonstrated with two real-world case studies. Results reveal the feasibility and advantages of empowering traffic operations and safety analysis with transportation big data.enDissertationCivil engineeringTransportationCloud ComputingTraffic OperationsTraffic SafetyTransportation Big DataTravel Behavior