Traffic analysis is vital for enhancing the performance of transportation systems, where continuous evaluation of traffic states helps responsible road authorities detect and address issues. High-quality traffic data is key to analysis, as it aids in planning and investments. Traditionally, traffic data collection has been costly and limited. Nowadays, connected vehicles and mobile phones have transformed this process, enabling traffic data collection across large geographic regions without the need for dedicated measurement devices. The availability of large-scale and detailed traffic data allows for in-depth analysis using mathematical models. This thesis develops models to utilize available traffic data for transportation system improvements, aiming to enhance traffic conditions and road user experience. It utilizes data from link flows and travel times, applying models over large geographic areas. The thesis addresses transportation engineering issues through data-driven methods. The thesis proposes two methods for allocating electric vehicle charging stations using optimization and route sampling techniques. It introduces a new index for assessing travel time reliability. It shows how clustering analysis of descriptive travel time statistics can be used to detect different traffic states. Furthermore, this thesis presents a statistical model to estimate link flow propagation using measured link flow data, analyzing traffic influence across surrounding areas. The thesis also uses traffic simulation, focusing on combining speed cameras and probe vehicles for data collection and developing a model to identify probable routes based on hourly link flows. The thesis results highlight the importance of data-driven models in optimizing transportation systems and improving road user travel experiences.